G.Pass, R.Zabih, J.Miller. Comparing images using color coherence vectors. ACM Multimedia. Boston, MA, 1996. Home/Search Document Details and Download
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Adaptively Discovering Meaningful Patterns in High-Dimensional.. - Wu, Zhang (2003) (Correct)
....In addition to its attribute values, every data point also has a class value between 0 and 9. COREL: This data set contains 31,438 data points with 179 attributes, where each point represents a color image using five image features: the first one is a 64 attribute color coherence vector [19] in the HSV color space; the second one is a 9 attribute color moments [18] extracted from the L a b color space; the third one is a 10 attribute wavelet based texture descriptor [22] the fourth one is a 64attribute edge coherence histogram [6] and the fifth one is a 32 attribute Fourier shape ....
Greg Pass, Ramin Zabih, and Justin Miller. Comparing images using color coherence vectors. In Proceedings of ACM Multimedia 96, pages 65--73, Boston MA USA, 1996.
Using Thesaurus To Model Keyblock-Based Image Retrieval - Lei Zhu Chun (2001) (1 citation) (Correct)
....as well as the evaluation is non trivial. Because the images are in favor of the color feature, we compare the performance of the thesaurus based histogram model with the traditional color histogram proposed by Swain and Ballard [6] and the color coherent vector (CCV) proposed by Pass and Zabih [5], as well as the histogram model [3] in the keyblock framework. To generate the keyblocks, we selected 621 images as the training set. Three block sizes, 2 2, 4 4 and 8 8, are used. For each block size, experiments have been performed to generate the codebooks of three different sizes ....
G. Pass, R. Zabih, and J. Miller. Comparing images using color coherence vectors. In Proceedings of ACM Multimedia 96, pages 65--73, Boston MA USA, 1996.
Feature Histograms for Content-Based Image Retrieval - Siggelkow (2002) (3 citations) (Correct)
....have been applied, that represent the histogram by moments [134, 74] or clusters [101] However, the histogram disregards the spatial constellation of the color. Thus very dissimilar images are considered similar. Various solutions to include positional information have been considered: [92, 132] both use the histogram technique and refine the histogram to include the absolute location of the pixel, or some kind of homogeneity information into the histogram. These techniques can work automatically as no preprocessing, like a segmentation, is required. However, incorporating the absolute ....
G. Pass, R. Zabih, and J. Miller. Comparing images using color coherence vectors. In ACM International Conference on Multimedia, pages 65--73, Boston, MA, November 1996.
Indexing Tamper Resistant Features for Image Copy Detection - Mork, Li, Chang, Cho, Li.. (1999) (1 citation) (Correct)
....used to index images in large image databases. Color histograms, however, do not contain spatial information about the colors. Thus, color histograms are inappropriate for image copy detection. Several methods have been proposed to remedy the shortcomings of color histograms. Pass and Zabih [9, 10] developed the color coherence vector (CCV) method, which partitions each color into a coherent and an incoherent portion. Hsu et al. 4] selected a set of dominant colors from an image and partitioned the image into rectangular regions, each containing one color. Smith and Chang [11] employed ....
G. Pass, R. Zabih, and J. Miller. Comparing images using color coherence vectors. Proceedings of the Fourth ACM Multimedia Conference, pages 65--73, 1996.
Sceneryanalyzer: A System Supporting Semantics-Based Image Retrieval - Song (Correct)
....image databases are demanded to provide open access to relevant information and products. Thus, content based image retrieval (CBIR) has become an active research area. A variety of techniques have been developed. In particular, content based image retrieval using lowlevel features such as color [41, 36, 26], texture [21, 35, 34, 40, 20] shape [42, 22, 14, 23, 24, 15, 10, 17, 46, 47, 33, 43, 32] and others [30, 38, 2, 16, 7] extracted from the images has been well studied. Various image querying systems including QBIC [11] VisualSeek [36] PhotoBook [27] and Virage [5] have been built based on the ....
Greg Pass, Ramin Zabih, and Justin Miller. Comparing images using color coherence vectors. In Proceedings of ACM Multimedia 96, pages 65-73, Boston MA USA, 1996.
Finding Structure in Consumer Videos by Probabilistic . . . - Gatica-Perez, al. (2002) (Correct)
....a technique similar to that described in [34] for shot boundary detection. Furthermore, subshots of very short duration are discarded, as they often correspond to fast camera pannings. In the second place, from the various image representations that have been proposed for content based retrieval [29], 48] 43] 24] 30] 49] 39] 44] we have selected joint histograms [30] These are simple estimations of multivariate distributions that combine color and scene structure information, and have shown to signi cantly improve color only image retrieval. Investigated features included: 1. ....
....and the HSV space (vectorquantized to 1024 colors [44] 2. Color ratios (known to be illumination invariant) non linearly quantized to 32 levels [1] 3. Edge based features, including edge density and edge directions [43] Other features can be tested and added in a straightforward fashion [29]. The nal point consists in the de nition of the similarity measure. If the subshots s ik ; s jl are characterized by M and N random frames respectively, each represented by a joint histogram h ikm ; h jln , the similarity between two subshots is de ned as d(s ik ; s jl ) minfd (h ikm ; h ....
G. Pass, R. Zabih, and J. Miller, Comparing Images Using Color Coherence Vectors, in 4th ACM Conference on Multimedia, Boston, MA, Nov. 1996.
Analyzing Scenery Images by Monotonic Tree - Song, Zhang (2002) (2 citations) (Correct)
....urgent need to build efficient and effective image retrieval systems. Content based image retrieval (CBIR) offers a promising technology to address this need, and a variety of CBIR techniques have been developed. In particular, content based image retrieval using low level features such as color [43, 38, 27], texture [22, 37, 36, 42, 21] shape [44, 23, 13, 24, 25, 14, 10, 16, 48, 49, 35, 45, 34] and others [29, 39, 2, 15, 7] has been well studied. Various image querying systems, including QBIC [11] VisualSeek [38] PhotoBook [28] Netra [19] and Virage [5] have been built, using low level ....
....RETRIEVELIST. The precision and recall figures were then plotted to demonstrate the performance of SceneryAnalyzer. In Subsection 5. 2, performance with the COREL database will be used to compare SceneryAnalyzer with the keyblock model [17] traditional color histogram [43] color coherent vector [27], and wavelet (Haar and Daubechies) texture techniques [37, 41] All these methods accept only queries by example. The process of selecting query sets and calculating the precision recall for these methods can be illustrated with a typital case, where the sky feature is queried using the keyblock ....
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Greg Pass, Ramin Zabih, and Justin Miller. Comparing images using color coherence vectors. In Proceedings of ACM Multimedia 96, pages 65--73, Boston MA USA, 1996.
Distributed Image Indexing and Retrieval with Mobile Agents - Roth (1999) (Correct)
.... Presently, we are implementing a prototype based on the (yet unpublished) SeMoA (Secure Mobile Agents) platform and a watermarking system developed by the Institut f ur Graphische Datenverarbeitung in Darmstadt [5] and a content based retrieval mechanism based on color coherence vectors [7]. ....
PASS, G., ZABIH, R., AND MILLER, J. Comparing images using color coherence vectors. In Proc. ACM Conference on Multimedia (Boston, Massachusetts, U. S. A., November 1996).
The Bayesian Image Retrieval System, PicHunter.. - Cox, Miller.. (2000) (34 citations) Self-citation (Miller) (Correct)
....and careful experimental methods. Section II describes the theoretical basis for PicHunter and derives the necessary Bayesian update formulae. In order to implement the the 1Color has proven to be an image feature with some capability of retrieving images from common semantic categories [24] [25], 26] 27] 2s] 9] 29] oretical framework, it is necessary to decide upon a user interface and a model of the user. These are described in Sections III and IV. The user model is supported by psychophysical experiments that are also reported in Sec tion IV. In order to evaluate the ....
....vector of the RGB image after quantization into 4 x 4 x 4 64 color bins. This vector is the concatenation of two 64 bin histograms: one for coherent pixels and one for incoherent pixels. A coherent pixel is defined as one belonging to a large connected region with pixels of the same color [25]. B. Relative Versus Absolute Distance Criteria Relative distance criterion: In this scheme, the set Q Xq,Xq2, Xqc of selected images in the display Dr, as well as the set N = X, X,2, X,L of non selected images, play a role in approximating the user model term P(At [Ti, Dr) by ....
G. Pass, R. Zabih, and J. Miller, "Comparing images using color coherence vectors," in Fourth ACM Conference on Multimedia, Boston, Massachusetts, November 1996, pp. 65-73.
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G.Pass, R.Zabih, J.Miller. Comparing images using color coherence vectors. ACM Multimedia. Boston, MA, 1996.
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G. Pass, R. Zabih, and J. Miller, "Comparing images using color coherence vectors," in Proc. 4th ACM Conference on Multimedia, Boston, MA, Nov. 1996, http://simon.cs.cornell.edu/Info/People/rdz/rdz.html.
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G. Pass, R. Zabih, and J. Miller, "Comparing images using color coherence vectors," in Proc. ACM Multimedia, 1996, pp. 65--73.
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Greg Pass, Ramin Zabih, and Justin Miller. Comparing images using color coherence vectors. In Proceedings of ACM Multimedia 96, pages 65--73, Boston MA USA, 1996.
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Greg Pass, Ramin Zabih, and Justin Miller. Comparing images using color coherence vectors. In Proceedings of ACM Multimedia 96, pages 65--73, Boston MA USA, 1996.
TREC-10 Experiments at University of Maryland CLIR and.. - Darwish, Doermann.. (2001) (Correct)
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Pass G, Zabih R & Miller J (1996) Comparing images using color coherence vectors. Proc. Fourth ACM International Conference on Multimedia, Boston, MA, 65-73.
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Greg Pass, Ramin Zabih, and Justin Miller. Comparing images using color coherence vectors. In Proc. ACM Conference on Multimedia, Boston, Massachusetts, U. S. A., November 1996.
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G.Pass, R.Zabih and J.Miller. "Comparing images using color coherence vectors". Proc. ACM Conf. On Multimedia, pp.65-73, Boston, USA, 1996.
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G. Pass, R. Zabih & J. Miller. Comparing images using color coherence vectors. Proceedings of ACM Multimedia' 96, 65-73, 1996.
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G. Pass, R. Zabih, and J. Miller. Comparing images using color coherence vectors. In Proceedings of ACM Multimedia 96, pages 65--73, Boston MA USA, 1996.
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Pass G., Zabih R. & Miller J. (1996). Comparing Images Using Color Coherence Vectors. Proceedings of ACM Multimedia '96, Boston, Massachusetts, Nov, 65-73.
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G. Pass, R. Zabih, and J. Miller. Comparing Images Using Color Coherence Vectors. In ACM Multimedia, 65-73, 1996.
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G. Pass, R. Zabih, and J. Miller. Comparing images using color coherence vectors. In Proc. of ACM Multimedia Intl. Conf., pages 65--73, 1996.
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