Abstract
The comparison of digital images to determine their degree of similarity is one of the fundamental problems of computer vision. Many techniques exist which accomplish this with a certain level of success, most of which involve either the analysis of pixel-level features or the segmentation of images into sub-objects that can be geometrically compared. In this paper we develop and evaluate a new variation of the pixel feature and analysis technique known as the color correlogram in the context of a content-based image retrieval system. Our approach is to extend the autocorrelogram by adding multiple image features in addition to color. We compare the performance of each index scheme with our method for image retrieval on a large database of images. The experiment shows that our proposed method gives a significant improvement over histogram or color correlogram indexing, and it is also memory-efficient.
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A. Williams’ work was supported by Trinity College Student Research Grant Program.
P. Yoon’s work was based upon work supported by NASA EPSCoR Core Funding Program.
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Williams, A., Yoon, P. Content-based image retrieval using joint correlograms. Multimed Tools Appl 34, 239–248 (2007). https://doi.org/10.1007/s11042-006-0087-2
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DOI: https://doi.org/10.1007/s11042-006-0087-2