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Content-Based Image Retrieval Using Error Diffusion Block Truncation Coding Features - 2015

PROJECT TITLE:

Content-Based Image Retrieval Using Error Diffusion Block Truncation Coding Features - 2015

ABSTRACT:

This paper presents a new approach to index color images using the features extracted from the error diffusion block truncation coding (EDBTC). The EDBTC produces two color quantizers and a bitmap image, that are more processed using vector quantization (VQ) to come up with the image feature descriptor. Herein 2 features are introduced, particularly, color histogram feature (CHF) and bit pattern histogram feature (BHF), to live the similarity between a question image and also the target image in database. The CHF and BHF are computed from the VQ-indexed color quantizer and VQ-indexed bitmap image, respectively. The distance computed from CHF and BHF will be utilized to measure the similarity between 2 pictures. As documented within the experimental result, the proposed indexing methodology outperforms the previous block truncation coding primarily based image indexing and the other existing image retrieval schemes with natural and textural information sets. Therefore, the proposed EDBTC isn't only examined with good capability for image compression however also offers an efficient manner to index pictures for the content-based mostly image retrieval system.


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