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Content-Based Image Retrieval System using Feature Extraction of Image Objects  

Jung Seh-Hwan (LG전자기술원 Innovation Center Digital Vision Group)
Seo Kwang-Kyu (상명대학교 산업정보시스템공학과)
Publication Information
Journal of Korean Society of Industrial and Systems Engineering / v.27, no.3, 2004 , pp. 59-65 More about this Journal
Abstract
This paper explores an image segmentation and representation method using Vector Quantization(VQ) on color and texture for content-based image retrieval system. The basic idea is a transformation from the raw pixel data to a small set of image regions which are coherent in color and texture space. These schemes are used for object-based image retrieval. Features for image retrieval are three color features from HSV color model and five texture features from Gray-level co-occurrence matrices. Once the feature extraction scheme is performed in the image, 8-dimensional feature vectors represent each pixel in the image. VQ algorithm is used to cluster each pixel data into groups. A representative feature table based on the dominant groups is obtained and used to retrieve similar images according to object within the image. The proposed method can retrieve similar images even in the case that the objects are translated, scaled, and rotated.
Keywords
Vector Quantization(VQ); Clustering; Content-based Image Retrieval System;
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