Y. Rui, T. S. Huang and S. Mehrotra, "Relevance Feedback Techniques in Interactive Content-based Image Retrieval," in Proc. IS&T and SPIE Storage and Retrieval of Image and Video Databases VI, San Jose, CA, USA, Jan. 1998.  Home/Search   Document Details and Download   Summary   Related Articles   Check

....based on the quality of the features extracted from images and the ability of the user to provide a good query. Relevance feedback can be richer than this. In particular, the information the user provides need not be expressible in the query language, but may entail modifying feature weights [22] or constructing new features on the fly [23] PicHunter takes this idea further with a Bayesian approach, representing its uncertainty about the user s goal by a probability distribution over possible goals. This Bayesian approach to the problem was pioneered by Cox et al. 3] With an explicit ....

Y. Rui, T. S. Huang, and S. Mehrotra, "Relevance feedback techniques in interactive content-based image retrieval," in Proc. of ISFJT and SPIE Storage and Retrieval of Image and Video Databases VI, San Jose, CA, January 1998.

.... yet artistically inclined user this nexibility manifests itself in allowing the generation of complex queries reinacting what is important or unimportant (i.e. inlcusion or exclusion of features within a hybrid query) This property can be advantageous when the technique of Relevance Feedback [10] is introduced into the CBIR system. The use of this concept would allow for desirable or undesirable features to be interactively, and iteratively promoted or culled during retrieval. Successive feedback queries would thus strive to achieve two goals; conform to each user s concept of an ideal ....

Y. Rui, T. Huang, S. Mehrotra, "Relevance Feedback Techniques in Interactive Content-Based Image Retrieval", SPIE, Vol. 3312, 1997.

....for applications such asimag editing analysis andimag publications etc. In addition, recent e#orts incombining the low level features with some type ofhig##BfbT information proves to be useful towards information retrieval from the imag content. Representative examples include relevance feedback [2,3], user intention prediction via Bayesian models [4] and semantic information extraction from low level features [5] etc. On top of that, numerous semantic features can also be added to provide a wide rang ofchoices for manual control and selection before any content based retrieval is even ....

Y. Rui, T.S.Huang S. Metrotra, Relevance feedback techniques in interactive content-basedimag retrieval, Proceeding ofIS& T and SPIEStorag and Retrieval of Imag and Video Databases VI, San Juan, PR, June 1997, pp. 762--768.

....and index artefacts. A variety of classification and indexing methods are described and used by developers, e.g. facets [12] keywords, enumerated schemes [21] multimedia properties [16, 20] or media indexes [7] Incorporate relevance feedback to deal with individual perception and preferences [15]. Resulting Context High quality artefacts that are properly classified and indexed can be easily retrieved and then reused within an organisation. For example, in an educational institution where some teachers lack sufficient experience in multimedia design, teaching staff could benefit from ....

....development task, but the suitability cannot be properly judged without taking use context into consideration [8, 22] Subjectivity. Also formulating and satisfying requirements describing intended use context might be hard due to individual perception of and preferences for multimedia properties [15]. Solution Development with reuse focuses on constructing products with the utilisation of reusable components drawn from the repository. Multimedia developer should apply intended use context in order to improve selected and rejected artefacts classification: Provide relevance feedback on ....

Rui, Y., Huang, T. S., and Mehrotra, S. Relevance Feedback Techniques in Interactive Content-Based Image Retrieval. in IS&T and SPIE Conference on Storage and Retrieval for Image and Video Databases VI. 1998. San Jose, CA, USA: SPIE, http://www.research.microsoft.com/~yongrui/html/publication.html.

....data to modify the query feature vector and give higher weights to the common components of the correlogram found in the relevant images. A similar approach was used in Rui et. al [23] on image texture feature representation. Instead of modifying feature level descriptions directly, Rui et. al [24] examined how relevance information may be used to optimize the ratios of combining various features in a multi feature image retrieval system. The user s relevance judgments were used to update these ratios dynamically in order to obtain a better way of combining the various features. In another ....

Y. Rui, T.S. Huang, and S. Mehrotra. Relevance feedback techniques in interactive contentbased image retrieval. In Proceedings of IS&T and SPIE Storage and Retrieval of Image and Video Databases VI, 1998.

....to modify the query feature vector and give higher weights to the common components of the correlogram found in the relevant images. A similar approach was used in Rui et al. [20] on image texture feature representations. Instead of modifying feature level descriptions directly, Rui et al. [21] examined how relevance information may be used to optimise the ratios of combining various features in an integrated content based image retrieval system. The user s relevance judgements are used to update these ratios dynamically to obtain a better way of combining the various features. In ....

Rui Y., Huang T.S. and Mehrotra S. Relevance Feedback Techniques in Interactive Content-Based Image Retrieval. Proceedings of IS&T and SPIE Storage and Retrieval of Image and Video Databases VI, San Jose, California, Jan. 1998.

....of the retrieval. From a user s perspective, searching for images in similarity search systems typically involves several steps. In the first few steps, a user refines his or her query with the help of relevance feedback until the query matches the information need sufficiently good (cf. MARS [RHM98] CHARIOT [The00] In the final step of the search process, the archive is extensively searched for (all) relevant images. Obviously, retrieval effectiveness in the first few steps is not so important as retrieval efficiency. In the final step, on the other hand, result quality plays the key ....

Y. Rui, T. Huang, and S. Mehrotra. Relevance Feedback Techniques in Interactive Content-Based Image Retrieval. In Storage and Retrieval for Image and Video Databases (SPIE), pages 25--36, San Jose, California, USA, Jan. 1998.

.... it must have 3 digit, e.g. 002, 100, 030, the relevanceI value may be a if all relevant images are used for the comparation process, or may be m if the mean of the query image is used, readWeights is 0 when the weights are not used, or 1 otherwise, weightsType is s (Mars type [4, 7, 6]) or m (rover type [8] the useWeightRange default value is 0 (false) otherwise is 1, the weightRange default value is 0.10, the exponent default value is 0 (this means that all the descriptors have the same weight; value 1 means that weight = 1=sqrt( value 2 means that weight = 1= ....

Y. Rui, T.S. Huang, and S. Mehrotra. Relevance Feedback Techniques in Interactive Content-Based Image Retrieval. In Proc. of IS&T and SPIE Storage and Retrieval of Image and Video Databases VI, San Jose, CA, January 24-30 1998.

....based on the quality of the features extracted from images and the ability of the user to provide a good query. Relevance feedback can be richer than this. In particular, the information the user provides need not be expressible in the query language, but may entail modifying feature weights [22] or constructing new features on the y [23] PicHunter takes this idea further with a Bayesian approach, representing its uncertainty about the user s goal by a probability distribution over possible goals. This Bayesian approach to the problem was pioneered by Cox et al. 3] With an explicit ....

Y. Rui, T. S. Huang, and S. Mehrotra, \Relevance feedback techniques in interactive content-based image retrieval," in Proc. of IS&T and SPIE Storage and Retrieval of Image and Video Databases VI, San Jose, CA, January 1998.

....matrix. The domain layer comprises user defined information that represent physical objects or concepts that can be translated in terms of one or more features in the lower layers. The domain event layer allows events computed from image sequences or videos to be defined as queriable entities. Rui et al. 1998) proposed an interactive approach to CBIR. Their approach allows the user to submit a coarse initial query and continuously refine the information needed via so called relevance feedback . During the retrieval process, the high level query specification, and the subjectivity of perception are ....

....data model should also facilitate the retrieval process at various levels such as allowing the use of different similarity measures, ranking methods, feature presentations and enable user feedback. There are only a small number of approaches that can accommodate this. The approach presented in (Rui et al. 1998) is flexible enough to allow the user to submit a coarse initial query and continuously refine the information needed via relevance feedback . A document image model should aid in the conversion of paper documents to an electronic and retrievable form and enable efficient access to a document s ....

Rui Y, Huang TS & Mehrotra S (1998) Relevance feedback techniques in interactive content-based image retrieval. Proc. SPIE Storage and Retrieval for Image and Video Databases VI, San Jose, California, 25-36.

....available to the public and scientific communities. We have, for example, performed a basic evaluation of two such systems: the Query by Image and Video Content (QBIC) System [13] created by researchers at the IBM Almaden Research Laboratory, and the Multimedia Analysis and Retrieval System (MARS) [14] created by researchers at the University of Illinois at Urbana Champaign. These systems allow the user K.W.T. Correspondence) Email: tobinkwjr ornl.gov; WWW: http: www ismv.ic.ornl.gov; Telephone: 423 574 8521; Fax: 423 5746663 Prepared by OAK RIDGE NATIONAL LABORATORY, Oak Ridge, ....

Y. Rui, T.S. Huang, and S. Mehrotra, "Relevance Feedback Techniques in Interactive Content-based Image Retrieval", Storage and Retrieval for Image and Video Databases VI,, IS&T/SPIE's 10 th Annual International Symposium on Electronic Imaging: Science & Technology, San Jose Convention Center, San Jose, CA, January 24-30, 1998.

....approach can index more than 50 images per second on a Sun Sparc Ultra 1 machine. Recently, the focus of image indexing has also been shifted from finding the optimal features to constructing the interactive mechanisms capable of modeling human perception subjectivity. For instance, Rui et al. [22] investigate the relevancy feedback in order to determine the appropriate features and similarity measures for retrieval. In this section, we will mainly discuss the recent techniques in extracting features directly from the DCT domain for indexing and retrieval. 3.1 Color To reduce the amount ....

Y. Rui, T. S. Huang & S. Mehrotra, "Relevance Feedback Techniques in Interactive Content-Based Image Retrieval," Proc. SPIE Storage and Retrieval for Still Image and Video Database VI, vol. 3312, pp. 25-36, 1998.

....on neuroradiological image retrieval, where the approximate bilateral symmetry of normal human brains is exploited. 1 Introduction Existing content based image retrieval systems depend on general visual properties such as color and texture to classify diverse, two dimensional (2D) images [4, 12, 15, 11, 10, 13]. These general visual cues often fail to be effective discriminators for image sets taken within a single domain, where images have subtle, domain specific differences. Furthermore, global statistical color and texture measures do not necessarily reflect the meaning of an image. Databases ....

Y. Rui, T.S. Huang, and S. Mehrotra. Relevance feedback techniques in interactive content-based image retrieval. In SPIE/IS&T Conf. on Storage and Retrieval for Image and Video Databases VI. volume 3312, January 1998.

....is more meaningful to human perception than the texture feature representation alone, and significantly improves the retrieval performance. We compare the performance of our feature representation based on PIDs with Gabor texture feature representation [10] using interactive relevance feedback [14]. This is accomplished by incorporating the new feature into the data model of the CHITRA CBIR system we are developing. We also describe a new technique for performance comparison in CBIR systems. We measure the user s subjectivity on feature representations using an interactive relevance ....

....performance based on user s perception by giving weights to feature representations. If a feature representation agrees with the user perception, then the corresponding feature gets higher weights assigned. This is accomplished by interactive relevance feedback techniques as described below [14]. 5.2 Interactive Relevance Feedback System The image data is represented as a five tuple I m = I ; F; R; V; M , where I is the raw image data, F = ff i g a set of features. R = fr ij g, is the feature representation set ( each feature can have more than one representation) M represents ....

Yong Rui, Thomas S. Huang and Sharad Mehrotra. Relevance feedback techniques in interactive content based image retrieval. In Proc. of IS & T and SPIE Storage and Retrieval of Image and Video Databases VI, San Jose, CA, 1998.

....and or ambiguous [44] Since medical images form an essential and inseparable component of diagnosis, intervention and patient follow ups, it is natural to use medical images as front end index to retrieve relevant medical cases. Existing content based image retrieval systems, for example [13, 17, 36, 41, 35, 33, 38], depend on general visual properties such as color and texture to classify diverse, two dimensional (2D) images. These general visual cues often fail to be effective discriminators for image sets taken within a single domain, where images have subtle, domain specific differences. Furthermore, ....

....2.1 Content Based Image Retrieval Most content based image retrieval work uses general image features such as color, texture, or line segment histograms to classify diverse, two dimensional (2D) images. Much recent work has focused on fine tuning of a user s query based on user input or feedback [38, 21, 10]. The two main methods in the literature are (1) query point movement and (b) axis re weighting. To compare our work with the Rosetta system of [10] for example, one can see that in [10] the semantic category is defined on the fly by a set of query images, and the statistics of the query set are ....

Y. Rui, T.S. Huang, and S. Mehrotra. Relevance feedback techniques in interactive contentbased image retrieval. In SPIE/IS&T Conf. on Storage and Retrieval for Image and Video Databases VI. volume 3312, January 1998.

....of sequence number, time, content, and labeling in the similarity measure. However, it can also be through relevance feedback mechanisms provided in the user interface of the browsing environment [60, 61] While a number of techniques have used relevance feedback in content based image retrieval [62, 63, 64], we believe that the use of relevance feedback for browsing is a very di#erent problem which requires a fundamentally di#erent approach. 5.1 Similarity Pyramids for Video Browsing The structure of a similarity pyramid is illustrated in Figure 8. The similarity pyramid organizes large video ....

Yong Rui, Thomas S. Huang, and Sharad Mehrotra, "Relevance feedback techniques in interactive content-based image retrieval," in Proceedings of SPIE/IS&T Conference on Storage and Retrieval for Image and Video Databases VI, San Jose, CA, January 26-29 1998, vol. 3312, pp. 25--36.

.... a k nearest neighbor (k NN) algorithm on each individual feature and then merging them to get the overall answers [17] 14] 6] 7] Due to the subjective nature of top k queries, the answers returned by the system to a user query usually do not satisfy the user s need right away [18] 4] [23], 11] This can happen due to several reasons: The starting examples may not be the best ones to capture the information need (IN) of the user or the starting weights may not accurately capture the users perception or both. In this case, the user would like to refine the query and resubmit it in ....

....new query is called the refined query. In a QBE environment (e.g. multimedia databases) the user typically refines the query by finding, among the answers returned to the starting query, one or more objects that are closest to what she wants and requesting for more objects like those [22] [23], 18] 4] 11] Based on the user feedback, the system will compute the new query objects and the new weights and execute the refined query. Another way to refine the query is that the user explicitly modifies the perception model, i.e. she explicitly changes the weights of the features so as ....

[Article contains additional citation context not shown here]

Y. Rui, T. Huang, and S. Mehrotra, "Relevance Feedback Techniques in Interactive Content-Based Image Retrieval," Proc. IS & T and SPIE Storage and Retrieval of Image and Video Databases, 1998.

.... executing a k nearest neighbor (k NN) algorithm on each individual feature 2 and then merging them to get the overall answers [24, 21, 9, 10] Due to the subjective nature of top k queries, the answers returned by the system to a user query usually does not satisfy the user s need right away [25, 5, 32, 15]. This can happen due to several reasons: the starting examples may not be the best ones to capture the information need (IN) of the user or the starting weights may not accurately capture the users perception or both. In this case, the user would like to refine the query and resubmit it in order ....

....and the new query is called the refined query. In a QBE environment (e.g. multimedia databases) the user typically refines the query by finding, among the answers returned to the starting query, one or more objects that are closest to what she wants and requesting for more objects like those [30, 31, 32, 25, 5, 15]. Based on the user feedback, the system will compute the new query objects and the new weights and execute the refined query. Another way to refine the query is that the user explicitly modifies the perception model i.e. she explicitly changes the weights of the features so as to better capture ....

[Article contains additional citation context not shown here]

Y. Rui, T. Huang, and S. Mehrotra. Relevance feedback techniques in interactive content-based image retrieval. Proc. of IS&T and SPIE Storage and Retrieval of Image and Video Databases, 1998.

.... by first executing a nearest neighbor (k NN) algorithm on each individual feature 2 and then merging them to get the overall answers [17, 14, 7, 8] Due to the subjective nature of top queries, the answers returned by the system to a user query usually do not satisfy the user s need right away [18, 3, 23, 11]. This can happen due to several reasons: the starting examples may not be the best ones to capture the information need (IN) of the user or the starting weights may not accurately capture the users perception or both. In this case, the user would like to refine the query and resubmit it in order ....

....and the new query is called the refined query. In a QBE environment (e.g. multimedia databases) the user typically refines the query by finding, among the answers returned to the starting query, one or more objects that are closest to what she wants and requesting for more objects like those [22, 23, 18, 3, 11]. Based on the user feedback, the system will compute the new query objects and the new weights and execute the refined query. Another way to refine the query is that the user explicitly modifies the perception model i.e. she explicitly changes the weights of the features so as to better capture ....

[Article contains additional citation context not shown here]

Y. Rui, T. Huang, and S. Mehrotra. Relevance feedback techniques in interactive content-based image retrieval. Proc. of IS&T and SPIE Storage and Retrieval of Image and Video Databases, 1998.

.... first executing a k nearest neighbor (k NN) algorithm on each individual feature 2 and then merging them to get the overall answers [17, 14, 7, 8] Due to the subjective nature of top k queries, the answers returned by the system to a user query usually do not satisfy the user s need right away [18, 3, 23, 11]. This can happen due to several reasons: the starting examples may not be the best ones to capture the information need (IN) of the user or the starting weights may not accurately capture the users perception or both. In this case, the user would like to refine the query and resubmit it in order ....

....and the new query is called the refined query. In a QBE environment (e.g. multimedia databases) the user typically refines the query by finding, among the answers returned to the starting query, one or more objects that are closest to what she wants and requesting for more objects like those [22, 23, 18, 3, 11]. Based on the user feedback, the system will compute the new query objects and the new weights and execute the refined query. Another way to refine the query is that the user explicitly modifies the perception model i.e. she explicitly changes the weights of the features so as to better capture ....

[Article contains additional citation context not shown here]

Y. Rui, T. Huang, and S. Mehrotra. Relevance feedback techniques in interactive content-based image retrieval. Proc. of IS&T and SPIE Storage and Retrieval of Image and Video Databases, 1998.

....mechanisms for users to specify relative importance (weights) among features [10, 1] Overall similarity (distance) between an object and the query image is then computed as a weighted summation of similarities (distances) over the feature set. There are several shortcomings to such an approach [23, 21]. It places too much of a burden on users to formulate their exact information need. Users may find it difficult to express their query appropriately in terms of the provided features since they may not initially have a clear idea of their information need. Furthermore, there may be a mismatch ....

....query point movement. In the query point movement approach, a query is represented by a single point in a feature space and refinement process attempts to move that point toward the direction where relevant points were located. A query point movement approach has been explored in our previous work [21] as well as in MindReader [15] While the framework described in this paper can support query point movement, we instead focus on the query expansion technique. Unlike query point movement, query expansion does not assume that a query is represented as a point in a multidimensional space. Instead, ....

Yong Rui, Thomas S. Huang, and Sharad Mehrotra. Relevance feedback techniques in interactive content-based image retrieval. In Storage and Retrieval of Images/Video Databases VI, EI'98, 1998.

....few objects that are most similar to the submitted example (e.g. top 10 images that match the query image) The task of the DBMS is to incrementally return the top matches to the user as efficiently as possible. An important aspect of multimedia similarity retrieval is that of query refinement [31, 13, 24]. Due to the subjective nature of multimedia retrieval, it is unlikely that the answers returned to the starting query will satisfy the user s need right away. Rather, among the answers returned, the user may find one or more objects that are closer to what she had mind than the original example ....

....either of the 2 following ways: ffl Query Point Movement (QPM) In this model, the objects marked relevant by the user during feedback is represented by a single point in each feature space F : the weighted centroid. Effectively, for each F , the query point is moved towards the relevant objects [30, 32, 31, 13]. ffl Query Expansion (QEX) In this model, the relevant objects are represented by multiple points (called representatives [24] in each feature space giving rise to multipoint queries [24, 25, 26] The weight (wF i s) of any representative R in the multipoint query is proportional to the ....

[Article contains additional citation context not shown here]

Y. Rui, T. Huang, and S. Mehrotra. Relevance feedback techniques in interactive content-based image retrieval. Proc. of IS&T and SPIE Storage and Retrieval of Image and Video Databases, 1998.

....recent research emphasizes interactive systems with human in the loop. Representative works include the FourEyes system [13] using learning through user interaction, Netra [12] incorporating supervised learning for texture analysis, WebSEEk [18] for dynamic feature vector recomputation, and MARS [16] using a relevance feedback framework for content based retrieval. 2.2. High level Concepts and Low level Features Except for specific domains, general users prefer to use high level concepts in accessing information, including images and video. However, results of fully automatic image video ....

....standard will greatly benefit the inter operability of DIVL retrieval systems, as we have discussed in Section 2.5. 4.1. Synergistic Data Model in DIVL and MPEG7 Another aspect in considering the relationship between DIVL and MPEG 7 is to look at the hierarchical data model for images video. In [16], an image object model was presented as follows, O = O(D;F;R) 1) ffl D is the raw image data, e.g. a JPEG image. ffl F = ff i g is a set of low level visual features associated with the image object, such as color, texture, and shape. ffl R = fr ij g is a set of representations for a given ....

Yong Rui, Thomas S. Huang, and Sharad Mehrotra. Relevance feedback techniques in interactive contentbased image retrieval. In Proc. of IS&T SPIE Storage and Retrieval of Images/Video Databases VI, EI'98, 1998.

....the Internet, efficiency in the representation of images becomes a major factor affecting the overall system performance. To address some of these challenging research issues involved in multimedia databases, the MARS 1 project was started during the Spring of 1995 at the University of Illinois [9, 19, 23, 24, 26]. MARS supports retrieval of image, video, and audio data. A brief description of MARS is presented in Section 2. The main contribution offered in this paper is the development of a new representation of images, to support access to individual image regions directly in the compressed domain. We ....

....images that are best matches to the input query. The query language supported allows users to pose complex queries that are composed using low level image features as well as textual descriptions [15] Another unique feature of the MARS retrieval subsystem is that it supports relevance feedback [23, 24]. Relevance feedback is the process of automatically adjusting an existing query using the information fed back by the user about the relevance of previously retrieved objects such that the adjusted query is a better approximation to the information need of the user [3, 28] This approach greatly ....

[Article contains additional citation context not shown here]

Y. Rui, T. S. Huang, and S. Mehrotra. Relevance Feedback Techniques in Interactive Content-Based Image Retrieval. In Proceedings of the IS&T SPIE Storage and Retrieval of Images/Video Databases VI, EI'98, 1998.

....of images becomes a major factor affecting the overall system performance. To address some of these challenging research issues involved in multimedia databases, the MARS (Multimedia Analysis and Retrieval System) project was started during the Spring of 1995 at the University of Illinois [13, 22, 26, 28, 29]. MARS supports retrieval of image, video, and audio data. A brief description of MARS is presented in Section 2. The main contribution offered in this paper is the development of a new representation of images, to support access to individual image regions directly in the compressed domain. We ....

....images that are best matches to the input query. The query language supported allows users to pose complex queries that are composed using lowlevel image features as well as textual descriptions [20] Another unique feature of the MARS retrieval subsystem is that it supports relevance feedback [26, 28]. Relevance feedback is the process of automatically adjusting an existing query using the information fed back by the user about the relevance of previously retrieved objects such that the adjusted query is a better approximation to the information need of the user [6, 31] This approach greatly ....

[Article contains additional citation context not shown here]

Yong Rui, Thomas S. Huang, and Sharad Mehrotra. Relevance feedback techniques in interactive contentbased image retrieval. In Proc. of IS&T SPIE Storage and Retrieval of Images/Video Databases VI, EI'98,

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Y. Rui, T. S. Huang and S. Mehrotra, "Relevance Feedback Techniques in Interactive Content-based Image Retrieval," in Proc. IS&T and SPIE Storage and Retrieval of Image and Video Databases VI, San Jose, CA, USA, Jan. 1998.

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Rui, Y., Huang, T., and Mehrotra, S. Relevance feedback techniques in interactivecontent based image retrieval. In Proceedings of IS & TandSPIE Storage and Retrieval of Image and Video Databases (San Jose, CA, 1998), pp. 25--36.

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Y. Rui, T. S. Huang and S. Mehrotra, "Relevance Feedback Techniques in Interactive Content-based Image Retrieval," in Proc. IS&T and SPIE Storage and Retrieval of Image and Video Databases VI, San Jose, CA, USA, Jan. 1998.

No context found.

Rui, Y., Huang, T., and Mehrotra, S. Relevance feedback techniques in interactivecontent based image retrieval. In Proceedings of IS & TandSPIE Storage and Retrieval of Image and Video Databases (San Jose, CA, 1998), pp. 25--36.

No context found.

Y. Rui, T. S. Huang, and S. Mehrotra, "Relevance feedback techniques in interactive content-based image retrieval," in Storage and Retrieval for Image and Video Databases (SPIE), pp. 25--36, 1998.

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Y. Rui, T. S. Huang, S. Mehrotra, Relevance feedback techniques in interactive content-- based image retrieval, in: I. K. Sethi, R. C. Jain (Eds.), Storage and Retrieval for Image and Video Databases VI, Vol. 3312 of SPIE Proceedings, 1997, pp. 25--36.

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Y. Rui, T.S. Huang & S. Mehrotra. Relevance feedback techniques in interactive contentbased image retrieval. In Proceedings of IS&T and SPIE Storage and Retrieval of Image and Video Databases VI, 1998.

No context found.

Y Rui, T S Huang, and S Mehrotra. Relevance feedback techniques in interactive content-based image retrieval. In Storage and Retrieval for Image and Video Databases (SPIE), pages 25--36, 1998.

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Y. Rui, T. Huang, S. Mehrotra, "Relevance Feedback Techniques in Interactive Content-Based Image Retrieval

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Y. Rui, T. S. Huang, and S. Mehrotra, "Relevance Feedback Techniques in Interactive Content-Based Image Retrieval", Proceedings of the Conference on Storage and Retrieval of Image and Video Databases VI, (1S&T/SPIE-1998), San Jose, California, Jan. 1998.

No context found.

Y. Rui, T.S. Huang, and S. Mehrotra, \Relevance feedback techniques in interactive content-based image retrieval, " Proc. SPIE 3  312, pp. 25-36, 1998.

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Yong Rui, Thomas S. Huang, and Sharad Mehrotra, "Relevance feedback techniques in interactive contentbased image retrieval," in Proceedings of SPIE/IS&T Conference on Storage and Retrieval for Image and Video Databases VI, San Jose, CA, January 26-29 1998, vol. 3312, pp. 25--36.

No context found.

Y. Rui, T. S. Huang, and S. Mehrotra, "Relevance feedback techniques in interactive content-based image retrieval," in Proc. of SPIE/IS&T Conf. on Storage and Retrieval for Image and Video Databases VI, vol. 3312, pp. 25--36, (San Jose, CA), January 26-29 1998.

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