Image Saliency Detection Combining Sparse Reconstruction and Compactness

Aiming at the problem that existing image saliency detection algorithms can't correctly detect salient objects in complex environments, this paper proposes a method combining sparse reconstruction error and the compactness of image salient regions to calculate image saliency. Firstly, the main structure of the image is extracted to reduce background noise. Then the processed image is segmented into several superpixels. On one hand, the background dictionary is constructed by using the boundary superpixels. Each superpixel is projected on the dictionary for sparse reconstruction, and the reconstruction error is used to obtain a saliency map based on sparse reconstruction. On the other hand, the maps based on foreground seeds and background seeds are calculated separately via compactness of salient objects and fused. Finally, the saliency map obtained by the sparse reconstruction error and compactness are fused to get the final saliency map. The proposed algorithm is compared with 13 algorithms proposed in recent years on several public datasets. The experimental results show that the proposed algorithm is superior to all comparison algorithms