Optimization of discrete wavelet transform features using artificial bee colony algorithm for texture image classification

Abstract

Selection of appropriate image texture properties is one of the major issues in texture classification. This paper presents an optimization technique for automatic selection of multi-scale discrete wavelet transform features using artificial bee colony algorithm for robust texture classification performance. In this paper, an artificial bee colony algorithm has been used to find the best combination of wavelet filters with the correct number of decomposition level in the discrete wavelet transform. The multi-layered perceptron neural network is employed as an image texture classifier. The proposed method tested on a high-resolution database of UMD texture. The texture classification results show that the proposed method could provide an automated approach for finding the best input parameters combination setting for discrete wavelet transform features that lead to the best classification accuracy performance.

Texture Image Classification Using Wavelet Haar Approach and Gray Level Co-occurrence Matrix (GLCM)

“…Cattle, buffalo, and pigs are familiar sources of tainted ingredients, so their calculations may preserve social, religious, economic, and health purity [10]. Choosing an adequate image texture quality is one of the primary issues with texture classification [11]. To ascertain if raw meat texture traits are a reliable indicator of tenderness in beef [12].…”

High-resolution art recognition of modern packaging design and images based on texture image classification

“…Modern packaging design is a diversified design field that not only considers the aesthetics and functionality of the packaging but also adapts to the characteristics of various goods and market demands [5][6]. By using texture image classification technology, automatic classification and identification of different kinds of packaging can be achieved, improving the efficiency of packaging design while ensuring the quality and accuracy of packaging design [7][8]. Texture image classification techniques have become a research hotspot in image processing and have been widely used in many application areas [9].…”

“…Jun Shi. Applied Mathematics and Nonlinear Sciences, 9(1) (2024)[1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17] …”

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“…Using a high decomposition level on a short sequence will produce overlapping information, while using a low decomposition level on a long sequence will ignore much detailed information about the sequence ( [14]; [27]). Therefore, selecting the appropriate family type and DWT decomposition level for the feature representation of a class is important because these two parameters affect the classification performance ( [28]; [29]; [30]; [26]; [31]). Some studies used metaheuristic methods to optimise the selection of family types and DWT decomposition levels, such as genetic algorithms ( [32]; [33]), particle swarm optimisation ( [34]; [26], [32]), whale optimisation algorithm [35], and evolutionary quantum swarm algorithm [36].…”