Object detection based on visual attention and local descriptor
Lining Gao · Jisuanji gongcheng yu sheji · 2012
To detect objects in cluttered scenes,a novel detection model is proposed,which combines visual attention guidance and local descriptors representation.Firstly,the saliency map is computed and the SIFT local descriptors are extracted for input scene.After that,by matching keypoints of a hierarchical and saliency-based strategy,only the support local descriptors are selected to represent the distinctive features of pop-out objects.Simultaneously,the matching thresholds are adjusted with saliency weights.Qualitative and quantitative experiments on highly cluttered scenes are employed to validate the effectiveness and robustness of the proposed model.