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SIMPLIcity: Semantics-Sensitive Integrated Matching for Picture LIbraries
- IEEE Transactions on Pattern Analysis and Machine Intelligence
, 2001
"... The need for efficient content-based image retrieval has increased tremendously in many application areas such as biomedicine, military, commerce, education, and Web image classification and searching. We present here SIMPLIcity (Semanticssensitive Integrated Matching for Picture LIbraries), an imag ..."
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Cited by 551 (35 self)
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The need for efficient content-based image retrieval has increased tremendously in many application areas such as biomedicine, military, commerce, education, and Web image classification and searching. We present here SIMPLIcity (Semanticssensitive Integrated Matching for Picture LIbraries), an image retrieval system, which uses semantics classification methods, a wavelet-based approach for feature extraction, and integrated region matching based upon image segmentation. As in other regionbased retrieval systems, an image is represented by a set of regions, roughly corresponding to objects, which are characterized by color, texture, shape, and location. The system classifies images into semantic categories, such as textured-nontextured, graphphotograph. Potentially, the categorization enhances retrieval by permitting semantically-adaptive searching methods and narrowing down the searching range in a database. A measure for the overall similarity between images is developed using a region-matching scheme that integrates properties of all the regions in the images. Compared with retrieval based on individual regions, the overall similarity approach 1) reduces the adverse effect of inaccurate segmentation, 2) helps to clarify the semantics of a particular region, and 3) enables a simple querying interface for region-based image retrieval systems. The application of SIMPLIcity to several databases, including a database of about 200,000 general-purpose images, has demonstrated that our system performs significantly better and faster than existing ones. The system is fairly robust to image alterations.
Image retrieval: Current techniques, promising directions and open issues
- Journal of Visual Communication and Image Representation
, 1999
"... This paper provides a comprehensive survey of the technical achievements in the research area of image retrieval, especially content-based image retrieval, an area that has been so active and prosperous in the past few years. The survey includes 100+ papers covering the research aspects of image fea ..."
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Cited by 507 (15 self)
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This paper provides a comprehensive survey of the technical achievements in the research area of image retrieval, especially content-based image retrieval, an area that has been so active and prosperous in the past few years. The survey includes 100+ papers covering the research aspects of image feature representation and extraction, multidimensional indexing, and system design, three of the fundamental bases of content-based image retrieval. Furthermore, based on the state-of-the-art technology available now and the demand from real-world applications, open research issues are identified and future promising research directions are suggested. C ○ 1999 Academic Press 1.
Image retrieval: ideas, influences, and trends of the new age
- ACM COMPUTING SURVEYS
, 2008
"... We have witnessed great interest and a wealth of promise in content-based image retrieval as an emerging technology. While the last decade laid foundation to such promise, it also paved the way for a large number of new techniques and systems, got many new people involved, and triggered stronger ass ..."
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Cited by 485 (13 self)
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We have witnessed great interest and a wealth of promise in content-based image retrieval as an emerging technology. While the last decade laid foundation to such promise, it also paved the way for a large number of new techniques and systems, got many new people involved, and triggered stronger association of weakly related fields. In this article, we survey almost 300 key theoretical and empirical contributions in the current decade related to image retrieval and automatic image annotation, and in the process discuss the spawning of related subfields. We also discuss significant challenges involved in the adaptation of existing image retrieval techniques to build systems that can be useful in the real world. In retrospect of what has been achieved so far, we also conjecture what the future may hold for image retrieval research.
Blobworld: Image segmentation using Expectation-Maximization and its application to image querying
- IEEE Transactions on Pattern Analysis and Machine Intelligence
, 1999
"... Retrieving images from large and varied collections using image content as a key is a challenging and important problem. We present a new image representation which provides a transformation from the raw pixel data to a small set of image regions which are coherent in color and texture. This "B ..."
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Cited by 438 (10 self)
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Retrieving images from large and varied collections using image content as a key is a challenging and important problem. We present a new image representation which provides a transformation from the raw pixel data to a small set of image regions which are coherent in color and texture. This "Blobworld" representation is created by clustering pixels in a joint color-texture-position feature space. The segmentation algorithm is fully automatic and has been run on a collection of 10,000 natural images. We describe a system that uses the Blobworld representation to retrieve images from this collection. An important aspect of the system is that the user is allowed to view the internal representation of the submitted image and the query results. Similar systems do not offer the user this view into the workings of the system; consequently, query results from these systems can be inexplicable, despite the availability of knobs for adjusting the similarity metrics. By finding image regions whi...
Blobworld: A System for Region-Based Image Indexing and Retrieval
- In Third International Conference on Visual Information Systems
, 1999
"... . Blobworld is a system for image retrieval based on finding coherent image regions which roughly correspond to objects. Each image is automatically segmented into regions ("blobs") with associated color and texture descriptors. Querying is based on the attributes of one or two regions of ..."
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Cited by 375 (4 self)
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. Blobworld is a system for image retrieval based on finding coherent image regions which roughly correspond to objects. Each image is automatically segmented into regions ("blobs") with associated color and texture descriptors. Querying is based on the attributes of one or two regions of interest, rather than a description of the entire image. In order to make large-scale retrieval feasible, we index the blob descriptions using a tree. Because indexing in the high-dimensional feature space is computationally prohibitive, we use a lower-rank approximation to the high-dimensional distance. Experiments show encouraging results for both querying and indexing. 1 Introduction From a user's point of view, the performance of an information retrieval system can be measured by the quality and speed with which it answers the user's information need. Several factors contribute to overall performance: -- the time required to run each individual query, -- the quality (precision/recall) of each i...
Indoor-outdoor image classification
- IN IEEE INTL. WORKSHOP ON CONTENT-BASED ACCESS OF IMAGE AND VIDEO DATABASES
, 1998
"... We show how high-level scene properties can be inferred from classification of low-level image features, specifically for the indoor-outdoor scene retrieval problem. We systematically studied the features: (1) histograms in the Ohta color space (2) multiresolution, simultaneous autoregressive model ..."
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Cited by 269 (0 self)
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We show how high-level scene properties can be inferred from classification of low-level image features, specifically for the indoor-outdoor scene retrieval problem. We systematically studied the features: (1) histograms in the Ohta color space (2) multiresolution, simultaneous autoregressive model parameters (3) coefficients of a shift-invariant DCT. We demonstrate that performance is improved by computing features on subblocks, classifying these subblocks, and then combining these results in a way reminiscent of "stacking." State of the art single-feature methods are shown to result in about 75-86 % performance, while the new method results in 90.3 % correct classification, when evaluated on a diverse database of over 1300 consumer images provided by Kodak.
Color- and Texture-Based Image Segmentation Using EM and Its Application to Content-Based Image Retrieval
, 1998
"... Retrieving images from large and varied collections using image content as a key is a challenging and important problem. In this paper we present a new image representation which provides a transformation from the raw pixel data to a small set of image regions which are coherent in color and texture ..."
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Cited by 175 (11 self)
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Retrieving images from large and varied collections using image content as a key is a challenging and important problem. In this paper we present a new image representation which provides a transformation from the raw pixel data to a small set of image regions which are coherent in color and texture space. This so-called “blobworld ” representation is based on segmentation using the Expectation-Maximization algorithm on combined color and texture features. The texture features we use for the segmentation arise from a new approach to texture description and scale selection. We describe a system that uses the blobworld representation to retrieve images. An important and unique aspect of the system is that, in the context of similarity-based querying, the user is allowed to view the internal representation of the submitted image and the query results. Similar systems do not offer the user this view into the workings of the system; consequently, the outcome of many queries on these systems can be quite inexplicable, despite the availability of knobs for adjusting the similarity metric. 1
A survey of content-based image retrieval with high-level semantics
, 2007
"... In order to improve the retrieval accuracy of content-based image retrieval systems, research focus has been shifted from designing sophisticated low-level feature extraction algorithms to reducing the ‘semantic gap ’ between the visual features and the richness of human semantics. This paper attemp ..."
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Cited by 150 (5 self)
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In order to improve the retrieval accuracy of content-based image retrieval systems, research focus has been shifted from designing sophisticated low-level feature extraction algorithms to reducing the ‘semantic gap ’ between the visual features and the richness of human semantics. This paper attempts to provide a comprehensive survey of the recent technical achievements in high-level semantic-based image retrieval. Major recent publications are included in this survey covering different aspects of the research in this area, including low-level image feature extraction, similarity measurement, and deriving high-level semantic features. We identify five major categories of the state-of-the-art techniques in narrowing down the ‘semantic gap’: (1) using object ontology to define high-level concepts; (2) using machine learning methods to associate low-level features with query concepts; (3) using relevance feedback to learn users’ intention; (4) generating semantic template to support high-level image retrieval; (5) fusing the evidences from HTML text and the visual content of images for WWW image retrieval. In addition, some other related issues such as image test bed and retrieval performance evaluation are also discussed. Finally, based on existing technology and the demand from real-world applications, a few promising future research directions are suggested.
