Defining a Set of Features Using Histogram Analysis for Content Based Image Retrieval

Authors:
Jongan Park;Nishat Ahmad;Gwangwon Kang;Jun H. Jo;Pankoo Kim;Seungjin Park
Affiliations:
Dept of Information & Communications Engineering, Chosun University, Kwangju, South Korea;Dept of Information & Communications Engineering, Chosun University, Kwangju, South Korea;Dept of Information & Communications Engineering, Chosun University, Kwangju, South Korea;School of Information and Communication Technology Griffith University, Australia;Dept of Information & Communications Engineering, Chosun University, Kwangju, South Korea;Dept of Biomedical Engineering, Chonnam National University Hospital, Kwangju, South Korea
Venue:
ICIC '07 Proceedings of the 3rd International Conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence
Year:
2009

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Abstract

A new set of features are proposed for Content Based Image Retrieval (CBIR) in this paper. The selection of the features is based on histogram analysis. Standard histograms, because of their efficiency and insensitivity to small changes, are widely used for content based image retrieval. But the main disadvantage of histograms is that many images of different appearances can have similar histograms because histograms provide coarse characterization of an image. Hence we further refine the histogram using the histogram refinement method. We split the pixels in a given bucket into several classes just like histogram refinement method. The classes are all related to colors and are based on color coherence vectors. After the calculation of clusters using histogram refinement method, inherent features of each of the cluster is calculated. These inherent features include size, mean, variance, major axis length, minor axis length and angle between x-axis and major axis of ellipse for various clusters.