A Genetic Feature Selection for Texture Segmentation Using Wavelets

• Main Authors: Chen, Pon-Zen, 陳鵬仁
• Other Authors: Chin-Hsing Chen, Chin-Ming Tsai
• Format: Others
• Language: zh-TW
• Published: 1998
• Online Access: http://ndltd.ncl.edu.tw/handle/62061593740618411982

碩士 === 國立成功大學 === 電機工程學系 === 86 === Abstract Texture segmentation is an old and difficult problem as yet. TextureSegmentation is similar to texture classification except that the paternsof texture segmentation are pixels. Unlike texture classification, the patternsare samples of textures. Therefore, the difficulty of texture segmentaiton is rather higher than texture classification. In this thesis, we introduce a new feature, extrema fensity, extraced fromwavelet coefficients (filtered images). According to our experiments, we cansee that extrema density is more robust than the feature of Low's energymeasure wich is widely used in multichannel texture analysis. In fact, it isan encouraging result. Recently genetic algorithms (GAs) have advanced theoretically and have been successfully applied in many practical aspects. one of their applicationareas is the optimization problem. On this thesis, we applied GAs to search the subset, which results in the best segmentaiton results, form original feature space. For the objective function of GAs' part, we propose a measure,spatial separabvility measure, to estimate the actual error rate. The performance of segmentation obtained using the proposed measure approximatesto that gained using the actual error rate. In our experiments, the separable wavelet filters with tree-structured and pyramid-structured transform and nonseparable wavelet filters with tree-structured and pyramid-structured transform are compared. The segmentation results without feature selection, with feature seleciton using real error as the fitness and with feature selction using spatial separability measure as the fitness value are also compared. Experiments show that the nonseparablewavelet filters with tree-structure outperform other structures. Feature selection not only reduces the computation load but also improves the segmentaion results.