Texture Features for Browsing and Retrieval of Image Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
The anatomy of a large-scale hypertextual Web search engine
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Similarity space projection for web image search and annotation
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Proceedings of the 7th ACM SIGMM international workshop on Multimedia information retrieval
Clustering presentation of web image retrieval results using textual information and image features
IMSA'06 Proceedings of the 24th IASTED international conference on Internet and multimedia systems and applications
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MIR '06 Proceedings of the 8th ACM international workshop on Multimedia information retrieval
IGroup: web image search results clustering
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A survey of content-based image retrieval with high-level semantics
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IGroup: presenting web image search results in semantic clusters
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Ontology driven content based image retrieval
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Hierarchical clustering-based navigation of image search results
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JustClick: personalized image recommendation via exploratory search from large-scale Flickr images
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SIEVE: search images effectively through visual elimination
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A novel active learning approach for SVM based web image retrieval
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MMM'08 Proceedings of the 14th international conference on Advances in multimedia modeling
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APWeb'08 Proceedings of the 10th Asia-Pacific web conference on Progress in WWW research and development
Context-based clustering of image search results
KI'09 Proceedings of the 32nd annual German conference on Advances in artificial intelligence
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We consider the problem of clustering Web image search results. Generally, the image search results returned by an image search engine contain multiple topics. Organizing the results into different semantic clusters facilitates users' browsing. In this paper, we propose a hierarchical clustering method using visual, textual and link analysis. By using a vision-based page segmentation algorithm, a web page is partitioned into blocks, and the textual and link information of an image can be accurately extracted from the block containing that image. By using block-level link analysis techniques, an image graph can be constructed. We then apply spectral techniques to find a Euclidean embedding of the images which respects the graph structure. Thus for each image, we have three kinds of representations, i.e. visual feature based representation, textual feature based representation and graph based representation. Using spectral clustering techniques, we can cluster the search results into different semantic clusters. An image search example illustrates the potential of these techniques.