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Image Representation and Retrieval Using Support Vector Machine and Fuzzy C-means Clustering Based Semantical Spaces

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3 Author(s)
P. Bhattacharya ; Concordia University, Canada ; M. Rahman ; B. C. Desai

This paper presents a learning based framework for content-based image retrieval to bridge the gap between low-level image features and high-level semantic information presented in the images on semantically organized collections. Both supervised (probabilistic multi-class support vector machine) and unsupervised (fuzzy c-means clustering) learning based techniques are investigated to associate global MPEG-7 based color and edge features with their high-level semantical and/or visual categories. It represents images in a successive semantic level of information abstraction based on confidence or membership scores obtained from the learning algorithms. A fusion-based similarity matching function is employed on these new image representations to rank and retrieve most similar images compared to a query image. Experimental results on a generic image database with manually assigned semantic categories and on a medical image database with different modalities and examined body parts demonstrate the effectiveness of the proposed approach compared to the commonly used Euclidean distance measure on MPEG-7 based descriptors.

Published in:

18th International Conference on Pattern Recognition (ICPR'06)  (Volume:2 )

Date of Conference:

20-24 Aug. 2006