Supervised Texture Classification Using a Novel Compression-Based Similarity Measure

TL;DR

This paper introduces a new compression-based dissimilarity measure using MPEG-1 encoding for supervised texture classification, improving accuracy and speed over existing methods.

Contribution

It presents a novel MPEG-1 based dissimilarity measure that considers spatial pixel relationships, enhancing texture classification performance and computational efficiency.

Findings

Significantly improves classification accuracy on Brodatz and outdoor images.

Reduces computation time by approximately 40%.

Effective for both small and large image patches.

Abstract

Supervised pixel-based texture classification is usually performed in the feature space. We propose to perform this task in (dis)similarity space by introducing a new compression-based (dis)similarity measure. The proposed measure utilizes two dimensional MPEG-1 encoder, which takes into consideration the spatial locality and connectivity of pixels in the images. The proposed formulation has been carefully designed based on MPEG encoder functionality. To this end, by design, it solely uses P-frame coding to find the (dis)similarity among patches/images. We show that the proposed measure works properly on both small and large patch sizes. Experimental results show that the proposed approach significantly improves the performance of supervised pixel-based texture classification on Brodatz and outdoor images compared to other compression-based dissimilarity measures as well as approaches performed in feature space. It also improves the computation speed by about 40% compared to its rivals.