Gray Level Co-Occurrence Matrices: Generalisation and Some New Features

TL;DR

This paper introduces a new trace feature derived from Gray Level Co-occurrence Matrices (GLCM) for improved texture analysis in image retrieval, extending GLCM to n-dimensional images and demonstrating superior performance over traditional Haralick features.

Contribution

It proposes a novel trace feature from GLCM and extends GLCM to n-dimensional images, enhancing texture analysis in CBIR applications.

Findings

Trace features outperform Haralick features in CBIR tasks.

Extension of GLCM to n-dimensional images broadens its applicability.

New features improve texture analysis accuracy.

Abstract

Gray Level Co-occurrence Matrices (GLCM) are one of the earliest techniques used for image texture analysis. In this paper we defined a new feature called trace extracted from the GLCM and its implications in texture analysis are discussed in the context of Content Based Image Retrieval (CBIR). The theoretical extension of GLCM to n-dimensional gray scale images are also discussed. The results indicate that trace features outperform Haralick features when applied to CBIR.