Texture classification based on co-occurrence matrix and self-organizing map

Abstract

This article presents a hybrid approach for texture based image classification using the gray-level co-occurrence matrices (GLCM) and self-organizing map (SOM) methods. The GLCM is a matrix of how often different combinations of pixel brightness values (grey levels) occur in an image. The GLCM matrices extracted from an image database are processed to create the training data set for a SOM neural network. The SOM model organizes and extracts prototypes from processed GLCM matrices. This paper proposes a novel strategy to index match scores by searching through prototypes. A benchmark data set is used to demonstrate the usefulness of the proposed methodology. The evaluation of performance is based on accuracy in the framework of a Monte Carlo experience. This approach is compared with several classifiers in Li et al [1]. The experimental results on the Brodatz texture image database demonstrate that the proposed method is encouraging with an average successful rate of 97%.

BiomassID: A biomass type identification system for mobile devices

“…Some of the recent methods that are based on such features are (Ionescu et al, 2014a;de Almeida et al, 2010;Backes et al, 2012). A hybrid approach for texture-based image classification using GLCM and self-organizing maps is proposed in (de Almeida et al, 2010).…”

A new FPGA Accelerator Based on Circular Buffer Unit Per Orientation for a Fast and Optimised GLCM and Texture Feature Computation

“…The GLCM is an effective method of Haralick texture feature extraction, which is focus in texture analysis methods [1,2], image retrieval [3], image classification [4], image segmentation [5], and image recognition [6].…”

Identification of Textile Defects Based on GLCM and Neural Networks

“…The statistical features of GLCM are based on gray level intensities of the image. Such features of the GLCM are useful in texture recognition [4], image segmentation [5] [6], image retrieval [7], color image analysis [8], image classification [9] [10], object recognition [11] [12] and texture analysis methods [13] [14] etc. The statistical features are extracted from GLCM of the textile digital image.…”