关键词: 场景识别, 视觉词典, K-means聚类, 谱聚类, 互信息

Abstract: Generally，the K-means clustering method is applied to generate visual dictionary. However, on the one hand this unsupervised learning does not make use of the priori information of category. On the other hand, the own limitations of K-means clustering result in poor performance of visual dictionary. Aiming at this problem, this paper presents a new visual dictionary construction algorithm based on spectral clustering. The training samples are divided according to the category information firstly and carry out feature selecting using dynamic mutual information. And then it generates the final visual dictionary by spectral clustering in the feature space. This method not only takes advantage of the category information but also the advantages of spectral clustering fully and effectively solves the problems caused by high dimensionality and structural complexity of feature space. The experiments on Scene-15 database prove the effectiveness of the proposed method.

Key words: scene recognition, visual dictionary, K-means clustering, spectral clustering, mutual information