Content-based image retrieval using Mix histogram

TL;DR

This paper introduces a novel mix histogram feature that combines color and edge orientation information to improve content-based image retrieval accuracy, outperforming existing methods.

Contribution

The paper proposes a new mix histogram feature that effectively integrates color and edge orientation data for enhanced image similarity measurement.

Findings

Outperforms existing image retrieval methods

Effectively combines color and edge features

Improves retrieval accuracy

Abstract

This paper presents a new method to extract image low-level features, namely mix histogram (MH), for content-based image retrieval. Since color and edge orientation features are important visual information which help the human visual system percept and discriminate different images, this method extracts and integrates color and edge orientation information in order to measure similarity between different images. Traditional color histograms merely focus on the global distribution of color in the image and therefore fail to extract other visual features. The MH is attempting to overcome this problem by extracting edge orientations as well as color feature. The unique characteristic of the MH is that it takes into consideration both color and edge orientation information in an effective manner. Experimental results show that it outperforms many existing methods which were originally developed for image retrieval purposes.