Local graph regularized sparse reconstruction for salient object detection

Subspace representation based salient object detection has received increasing interests in recent years. However, due to the independent coding process of sparse reconstruction, the locality and the similarity among regions to be encoded are not explored. To preserve the locality and similarity of regions, a graph Laplacian regularization term is constructed as a smooth operator to alleviate the instability of the salient score in visual object. Then a new saliency map is calculated by incorporating this local graph regularizer into sparse reconstruction, which explicitly explores the local spatial structure of salient objects and thus obtains more uniform salient map. Moreover, we advance a heuristic object based dictionary from background superpixels, by which objects can be more accurately located. Experimental results on four large benchmark databases demonstrate that the proposed method performs favorably against fifteen recent state-of-the-art methods in terms of five evaluation criterions.