Genetic-based optimal encoding for image clustering with texture-based features

• Main Authors: Li-wei Lu, 盧立偉
• Other Authors: Chih-Hung Wu
• Format: Others
• Language: zh-TW
• Published: 2013
• Online Access: http://ndltd.ncl.edu.tw/handle/76177398596588526204

碩士 === 國立高雄大學 === 電機工程學系碩士班 === 101 === For image clustering, homogeneous and meaningful image pixels with specific features are clustered. The homogeneity of various features is usually calculated by the Euclidean distances among features. For features that have continuous variations, such as color, luminance, saturation, gradient of intensity, distance-based clustering can give effective results. When textures are used as features for clustering, an encoding scheme that describes the variations of textures in terms of distances can produce effective clustering results. This study proposes a genetic-based encoding method to deal with the abovementioned problem where the local binary pattern (LBP) is employed as the texture for clustering. The genetic algorithm (GA) is used to implement the optimal encoding scheme of LBP-based textures. In the encoding scheme, similar LBP-textures are required to have shorter distances, and vice versa. The GA process is separated into two stages. The first stage arranges the locations for all LBP patterns so that they can have continuous variations. The second stage assigns each LBP pattern a unique integer in a manner that similar (dissimilar) patterns have short (long) distances in the same Euclidean scale. A fitness function describing these requirements is defined. In this study, fuzzy c-means is used as the clustering method. Various encoding methods are compared with the proposed method. From the experimental results, the genetic-based encoding method finds a feasible set of encodes for LBP-based textures and improves the quality of image clustering. Some images are tested and the results are analyzed.