Multichannel Distributed Local Pattern for Content Based Indexing and Retrieval

TL;DR

This paper introduces MDLP, a new color feature descriptor combining local binary and mesh patterns for improved content-based image retrieval across multiple datasets.

Contribution

The paper presents a novel multichannel distributed local pattern (MDLP) descriptor that enhances texture and color feature extraction for image retrieval.

Findings

MDLP outperforms existing descriptors on benchmark datasets.

Significant improvements in ARP and ARR metrics.

Effective multi-channel texture and color analysis.

Abstract

A novel color feature descriptor, Multichannel Distributed Local Pattern (MDLP) is proposed in this manuscript. The MDLP combines the salient features of both local binary and local mesh patterns in the neighborhood. The multi-distance information computed by the MDLP aids in robust extraction of the texture arrangement. Further, MDLP features are extracted for each color channel of an image. The retrieval performance of the MDLP is evaluated on the three benchmark datasets for CBIR, namely Corel-5000, Corel-10000 and MIT-Color Vistex respectively. The proposed technique attains substantial improvement as compared to other state-of- the-art feature descriptors in terms of various evaluation parameters such as ARP and ARR on the respective databases.