Shape Representation and Classification through Pattern Spectrum and Local Binary Pattern - A Decision Level Fusion Approach

TL;DR

This paper introduces a decision-level fusion method combining Pattern Spectrum and Local Binary Pattern for shape representation and classification, utilizing Earth Movers Distance for matching, tested on standard datasets with promising accuracy.

Contribution

The paper proposes a novel fusion approach of Pattern Spectrum and LBP for shape classification, enhancing retrieval accuracy with a decision-level fusion strategy and EMD metric.

Findings

Achieved high retrieval accuracy on Kimia-99, Kimia-216, and MPEG-7 datasets.

Demonstrated superiority over existing shape retrieval methods.

Validated effectiveness of fusion approach through comparative analysis.

Abstract

In this paper, we present a decision level fused local Morphological Pattern Spectrum(PS) and Local Binary Pattern (LBP) approach for an efficient shape representation and classification. This method makes use of Earth Movers Distance(EMD) as the measure in feature matching and shape retrieval process. The proposed approach has three major phases : Feature Extraction, Construction of hybrid spectrum knowledge base and Classification. In the first phase, feature extraction of the shape is done using pattern spectrum and local binary pattern method. In the second phase, the histograms of both pattern spectrum and local binary pattern are fused and stored in the knowledge base. In the third phase, the comparison and matching of the features, which are represented in the form of histograms, is done using Earth Movers Distance(EMD) as metric. The top-n shapes are retrieved for each query shape. The accuracy is tested by means of standard Bulls eye score method. The experiments are conducted on publicly available shape datasets like Kimia-99, Kimia-216 and MPEG-7. The comparative study is also provided with the well known approaches to exhibit the retrieval accuracy of the proposed approach.