Implementing agglomerative hierarchic clustering algorithms for use in document retrieval
Information Processing and Management: an International Journal
Fast and effective text mining using linear-time document clustering
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Grouper: a dynamic clustering interface to Web search results
WWW '99 Proceedings of the eighth international conference on World Wide Web
Learning to construct knowledge bases from the World Wide Web
Artificial Intelligence - Special issue on Intelligent internet systems
PicASHOW: pictorial authority search by hyperlinks on the Web
Proceedings of the 10th international conference on World Wide Web
SIMPLIcity: Semantics-Sensitive Integrated Matching for Picture LIbraries
IEEE Transactions on Pattern Analysis and Machine Intelligence
Visually Searching the Web for Content
IEEE MultiMedia
Combining Textual and Visual Cues for Content-based Image Retrieval on the World Wide Web
Combining Textual and Visual Cues for Content-based Image Retrieval on the World Wide Web
Japanese morphological analyzer using word co-occurrence: JTAG
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
Hierarchical clustering of WWW image search results using visual, textual and link information
Proceedings of the 12th annual ACM international conference on Multimedia
Image Clustering System on WWW using Web Texts
HIS '04 Proceedings of the Fourth International Conference on Hybrid Intelligent Systems
Visual pattern discovery using web images
MIR '06 Proceedings of the 8th ACM international workshop on Multimedia information retrieval
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The increasing prevalence of broadband Internet access is making it easier to obtain rich contents like images, and more people are attempting image retrieval.We focus on how to present web image retrieval results to users. Most retrieval results contain multiple topics. To offset this complexity, many papers have discussed text retrieval result clustering [11][14]. In result clustering, we cluster the documents according to their topics by using the distance of text similarity. To group web image retrieval results, we have to consider the differences between image retrieval and text retrieval. First, most images on the Web do not have any textual information, so we have to automatically extract textual information if we are to index web images by semantic information. Second, text retrieval shows users text snippets as results which may not contain the information that user wants; however, thumbnail images are direct reduced-size versions of the originals, so the user can clearly figure out if the original image is desired or not. So, we think that how to present retrieval results is an important task in web image retrieval.In this paper, we describe how to semantically classify image retrieval results for making web image retrieval more effective. Text classification based on machine learning is used to generate basic semantic information, and image features and textual features are used for cluster presentation. We propose methods for presenting the results of image retrieval through the application of clustering. Experiments show that our procedure is effective.