Open Access

A Robust Color Object Analysis Approach to Efficient Image Retrieval

EURASIP Journal on Advances in Signal Processing20042004:878369

Received: 20 December 2002

Published: 15 June 2004


We describe a novel indexing and retrieval methodology integrating color, texture, and shape information for content-based image retrieval in image databases. This methodology, we call CLEAR, applies unsupervised image segmentation to partition an image into a set of objects. Fuzzy color histogram, fuzzy texture, and fuzzy shape properties of each object are then calculated to be its signature. The fuzzification procedures effectively resolve the recognition uncertainty stemming from color quantization and human perception of colors. At the same time, the fuzzy scheme incorporates segmentation-related uncertainties into the retrieval algorithm. An adaptive and effective measure for the overall similarity between images is developed by integrating properties of all the objects in every image. In an effort to further improve the retrieval efficiency, a secondary clustering technique is developed and employed, which significantly saves query processing time without compromising retrieval precision. A prototypical system of CLEAR, we developed, demonstrated the promising retrieval performance and robustness in color variations and segmentation-related uncertainties for a test database containing general-purpose color images, as compared with its peer systems in the literature.

Keywords and phrases

content-based image retrievalfuzzy logicregion-based featuresobject analysisclusteringefficiency

Authors’ Affiliations

Department of Computer Science, State University of New York


© Zhang and Zhang 2004