Performance Evaluation of Different Techniques for texture Classification

TL;DR

This paper compares various texture classification techniques, focusing on wavelet transforms and co-occurrence matrices, evaluating their accuracy and computational efficiency for image analysis tasks.

Contribution

It provides a comparative analysis of texture classification methods, highlighting Haar wavelet as the most efficient in terms of performance and accuracy.

Findings

Haar wavelet outperforms other wavelets in classification efficiency.

Haar wavelet is more efficient than co-occurrence matrix in performance.

Co-occurrence matrix yields high accuracy but is computationally intensive.

Abstract

Texture is the term used to characterize the surface of a given object or phenomenon and is an important feature used in image processing and pattern recognition. Our aim is to compare various Texture analyzing methods and compare the results based on time complexity and accuracy of classification. The project describes texture classification using Wavelet Transform and Co occurrence Matrix. Comparison of features of a sample texture with database of different textures is performed. In wavelet transform we use the Haar, Symlets and Daubechies wavelets. We find that, thee Haar wavelet proves to be the most efficient method in terms of performance assessment parameters mentioned above. Comparison of Haar wavelet and Co-occurrence matrix method of classification also goes in the favor of Haar. Though the time requirement is high in the later method, it gives excellent results for classification accuracy except if the image is rotated.