Content-Based Image Retrieval Using Features Extracted From Halftoning-Based Block Truncation Coding

Jing-Ming Guo

doi:10.1109/TIP.2014.2372619

Return

Content-Based Image Retrieval Using Features Extracted From Halftoning-Based Block Truncation Coding

2015-03-01

PRE

Jing-Ming Guo *

Heri Prasetyo

DOI：10.1109/TIP.2014.2372619

Original

Original request for help

Save

Abstract

En 中文

This paper presents a technique for content-based image retrieval (CBIR) by exploiting the advantage of low-complexity ordered-dither block truncation coding (ODBTC) for the generation of image content descriptor. In the encoding step, ODBTC compresses an image block into corresponding quantizers and bitmap image. Two image features are proposed to index an image, namely, color co-occurrence feature (CCF) and bit pattern features (BPF), which are generated directly from the ODBTC encoded data streams without performing the decoding process. The CCF and BPF of an image are simply derived from the two ODBTC quantizers and bitmap, respectively, by involving the visual codebook. Experimental results show that the proposed method is superior to the block truncation coding image retrieval systems and the other earlier methods, and thus prove that the ODBTC scheme is not only suited for image compression, because of its simplicity, but also offers a simple and effective descriptor to index images in CBIR system.

Keywords:

Bit pattern feature

color co-occurrence feature

content-based image retrieval

ordered dither block truncation coding

AI Summary

Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.