Object proposals using SVM-based integrated model

Utilizing object proposals as a preprocessing procedure has been shown its significance in many multimedia computing tasks. Most state-of-the-art methods devoted to finding a generic objectness measure for rating the possibilities of the initial sliding windows with or without objects. In fact, the object criteria vary from one objectness measure to another, which leads to the definite bottleneck for the single method. By observing the performance of the state-of-the-art in the large dataset, an integrated objectness model is proposed in this paper by accumulating the advantages from selected state-of-the-art techniques. First, the initial bounding boxes are generated by the strategy as same as the method with the highest object detection rate and slowest intersection over union drop. Second, these candidate boxes are re-scored based on each method's objectness system. Then, a score feature is obtained for each bounding box. A support vector machines (SVM) is utilized to train a general model on the training set constructed from a series of score vectors and the probabilistic scores for the testing boxes are predicted according to the learned model. The final proposals are ranked on account of the predicted scores. The evaluation on the challenging PASCAL VOC 2007 dataset shows that the proposed method has dominant concentration with better performance compared to the single state-of-the-art method.