A Novel Local Features Based Salient Object Recognition Algorithm via Hybrid SVM-QPSO Model

As the salient objects extraction is of great importance in computer vision and multimedia information retrieval, this paper concentrates on the problem of salient object recognition using local features. Considering the rotational invariance performance of circular region is much better, we exploit a circular region to replace the rectangular region. To implement the salient object detection, the visual object classes should be constructed from training image dataset through SIFT features clustering. Furthermore, for a test image, the object class which the test image belonged to can be detected by interest points matching. Afterwards, the SIFT features clustering and local features matching process can be implemented through the proposed hybrid SVM-QPSO model. To promote the quality of parameter selection in SVM, we utilize the quantum behaved particle swarm optimization technique to select suitable SVM parameters. Finally, experiments are conducted to make performance evaluation using the MSRC dataset. Experimental results show that compared with other methods, the proposed algorithm can effectively detect salient objects in both object detecting precision and computing efficiency.