A Fast Framework for Objects Cursory Recognition in Cluster Scene Based on Visual Attention

This paper presents a real-time framework for objects cursory recognition in cluster scene based on visual attention. First, multi-scale image features are combined into a single saliency map. Then, k-means method is used to estimate the position of objects from cluster scene by saliency map. Finally, we construct global color feature vector for saliency regions and recognize the objects by their correlation coefficients with templates. Results shows that this framework is efficient for objects cursory recognition in random cluster scene.