关键词: 协同显著检测, 低秩矩阵分解, 聚类一致性, 相似矩阵

Abstract: In view of the shortcomings of existing cosaliency detection algorithm, an algorithm based on similar matrix decomposition model and clustering consistency is proposed. Firstly, saliency thresholding is performed to extract the saliency regions for each image by selecting salient superpixels based on different maps. Secondly, the histogram vector is constructed by the RGB color feature of the saliency region, and all the row vectors are combined into a feature matrix, then histogram??s similar matrix is constructed. Thirdly, the low rank matrix decomposition model is used to decompose the feature matrix to obtain sparse matrix and weighted value. Weight value and elemental saliency maps are fused to get the weighted saliency maps. Finally, the final cosaliency maps are obtained by combining the clustering consistency maps and weighted saliency maps. The experimental results on iCosegSub and iCoseg image datasets, demonstrate that the method has better precision- recall curve and higher precision.

Key words: cosaliency detection, low rank matrix decomposition, clustering consistency, similar matrix