Titlebook: Machine Learning and Statistical Modeling Approaches to Image Retrieval; Yixin Chen,Jia Li,James Z. Wang Book 2004 Springer Science+Busine

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Machine Learning and Statistical Modeling Approaches to Image Retrieval

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1871-7500 essing: the World Wide Web is becoming an enormous and expanding distributed digital library. Along with the development of the Web, image indexing and retrieval have grown into research areas sharing a vision of intelligent agents. Far beyond Web searching, image indexing and retrieval can potentia

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Image Retrieval and Linguistic Indexing,ing of images refers to the labeling of images using hundreds of possible linguistic terms. Image categorization puts an image into one of the predefined categories. Both tasks are important to CBIR, computer object recognition, and image understanding. This chapter provides a survey of the prior work in these areas.

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Image Retrieval and Linguistic Indexing,easure. Given a query image, target images are ranked according to the similarity with respect to the query. Different features and similarity measures have been discussed. In general, high feature similarity may not correspond to semantic similarity because of the semantic gap. We review two classe

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A Robust Region-Based Similarity Measure,e is represented by a set of segmented regions each of which is characterized by a fuzzy feature (fuzzy set) reflecting color, texture, and shape properties. As a result, an image is associated with a family of fuzzy features corresponding to regions. Fuzzy features naturally characterize the gradua

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