关键词: 有判别力的颜色选择, 中间层特征挖掘, 模式, 分类

Abstract: Mid-level features mining is effective in discovering the relation between different visual words, and then the mined patterns are used to replace the visual words for image representation. All the patches are used to discover the patterns in most of the recent methods, while mining the patterns in local areas are not considered. Mining the patterns in local areas can discover the patterns in different object parts, finally the image representations constructed based on these patterns can help to increase the classification accuracy. This paper proposes an effective color based hierarchical pattern mining method. The discriminative colors are used to divide the images into different levels, and then the patterns are discovered from the patches in each different level, finally the discovered patterns are used for the final image representation. Classification results are presented on Soccer, Flower 17 and Flower 102 datasets, and the experiments demonstrate that the proposed method can obtain satisfactory results on these datasets.

Key words: discriminative color selection, mid-level features mining, pattern, classification