Co-saliency Object Detection Via Multi-clustering And Multiple Instance Learning

With many image devices such as digital cameras and smart phones widely promoted,massive image data naturally generates,which is including a lot of redundant information.Saliency detection can extract areas of images which people are interested in,and the extracted areas will be processed by the computer priority.On the one hand saliency detection makes the data to be processed by computer greatly compressed and saves the computer's storage space;on the other hand it improves the computing efficiency.The saliency detection method has obtained many results,but it only applies to single image detection at present and does not apply to multi-images co-saliency detection.Co-saliency detection is generally used to find the same or similar significance areas from multiple images or multiple videos,which emphasizes saliency and high repetition rate.There are few studies in this region at present.This paper presents two co-saliency detection algorithms,which are described below.The first algorithm is based on multi-clustering fusion.We observe that the clustering accuracy is different for different image scenarios under the same number of clusters.When the scene in the picture is simple,less clustering number will be accurate.Otherwise,the complex scene will be detected accurately under larger clustering number.To choose the better clustering result automatically for different scenes,we present to design weights for different clustering weak co-saliency results.That is,first we perform multi-clustering by set different numbers for clusters and get the clustering accuracy by calculating the confidence in every number.Confidence value is higher,the corresponding weight is bigger.Second,after we get the weak maps under different clustering numbers,we use the confidence values to fuse the weak maps to get the strong co-saliency map.We get the final map by fusing the initial saliency map and strong co-saliency map.The second algorithm is based on multi-features fusion multiple instance learning.In this algorithm we propose that the co-saliency results detected by different features make different contributions to distinct scenes,which mainly depends on the similarity between foreground and background.The specific steps of the algorithm are as follows: first,extract the positive and negative bags.Then extract color,texture,contrast,VGG16 features of bags to train and test KI-SVM network.Multi-results are fused by the weights to generate strong co-saliency maps,and weights are calculated by low-rank recovery errors.After manifold ranking in image and propagation co-saliency value in group,we get the final co-saliency maps.The two algorithms all use the initial saliency map,which is based on the existing saliency detection algorithm.In the experimental part,the two algorithms are applied to the post-processing of existing saliency detection and we evaluate the performance in qualitative and quantitative parts.At the same time we compare our algorithms with other algorithms in two publicly co-saliency datasets.