Optimization of Texture Feature Extraction Algorithm

Abstract

Texture, the pattern of information or arrangement of the structure found in an image, is an important feature of many image types.In a general sense, texture refers to surface characteristics and appearance of an object given by the size, shape, density, arrangement, proportion of its elementary parts. Due to the signification of texture information, texture feature extraction is a key function in various image processing applications, remote sensing and content-based image retrieval. Texture features can be extracted in several methods, using statistical, structural, model-based and transform information, in which the most common way is using the Gray Level Co-occurrence Matrix (GLCM). GLCM contains the second-order statistical information of spatial relationship of pixels of an image. From GLCM, many useful textural properties can be calculated to expose details about the image content. However, the calculation of GLCM is very computationally intensive and time consuming. In this thesis, the optimizations in the calculation of GLCM and texture features are considered, different approaches to the structure of GLCM are compared. We also proposed parallel computing of GLCM and texture features using Cell Broadband Engine Architecture (Cell Processor). Experimental results show that our parallel approach reduces impressively the execution time for the GLCM texture feature extraction algorithm.