Image Classification by Integrating Color, Texture and Region

• Main Authors: Ya-Chuan, Cheng, 程雅娟
• Other Authors: Shu-Yuan, Chen
• Format: Others
• Language: zh-TW
• Published: 2001
• Online Access: http://ndltd.ncl.edu.tw/handle/46426359626823679304

碩士 === 元智大學 === 資訊工程學系 === 89 === A new classification method by integrating color, texture and region is proposed in this study. We adopt a color texture segmentation method that unifies color and texture features to obtain semantic regions. Image-based features related to color and local edges patterns are then used to prune irrelevant database images for each query image. The proposed region matching is then applied to each pair of the query image and the database images in the small plausible set. Thus, the dissimilarity of each pair can be calculated on the basis of the matching results. Finally, all the database images in the plausible set can be ranked in the ascending order of dissimilarity values. To achieve the classification goal, the k-NN rule is used to assign class label to the query image. The effectiveness and practicability of the proposed method has been demonstrated by various experiments.