Saliency quality assessment aims at estimating the objective quality of a saliency map without access to the ground-truth. Existing works typically evaluate saliency quality by utilizing information from saliency maps to assess its compactness and closedness while ignoring the information from image content which can be used to assess the consistence and completeness of foreground. In this letter, we propose a novel multi-information fusion network to capture the information from both the saliency map and image content. The key idea is to introduce a siamese module to collect information from foreground and background, aiming to assess the consistence and completeness of foreground and the difference between foreground and background. Experiments demonstrate that by incorporating image content information, the performance of the proposed method is significantly boosted. Furthermore, we validate our method on two applications: saliency detection and segmentation. Our method is utilized to choose optimal saliency map from a set of candidate saliency maps, and the selected saliency map is feeded into an segmentation algorithm to generate a segmentation map. Experimental results verify the effectiveness of our method.

In this paper, we improve upon the accuracy of existing tracklet generation methods by repairing tracklets based on their quality evaluation and detection propagation. Starting from object detections, we generate tracklets using three existing methods. Then we perform co-tracklet quality evaluation to score each tracklet and filtered out good tracklet based on their scores. A detection propagation method is designed to transfer the detections in the good tracklets to the bad ones so as to repair bad tracklets. The tracklet quality evaluation in our method is implemented by intra-tracklet detection consistency and inter-tracklet detection completeness. Two propagation methods; global propagation and local propagation are defined to achieve more accurate tracklet propagation. We demonstrate the effectiveness of the proposed method on the MOT 15 dataset

Multiple image group cosegmentation (MGC) aims at segmenting common object from multiple group of images, which is a new cosegmentation research topic. The existing MGC methods formulate MGC as label assignment problem (Markov Random Field framework), which is observed to be sensitive to parameter setting. Meanwhile, it is also observed that large object variations and complicated backgrounds dramatically decrease the existing MGC performance. To this end, we propose a new object proposal based MGC model, with the aim of avoiding tedious parameter setting, and improving MGC performance. Our main idea is to formulate MGC as new region proposal selection task. A new energy function in term of proposal is proposed. Two aspects such as the foreground consistency within each single image group, and the group consistency among image groups are considered. The energy minimization method is designed in EM framework. Two steps such as the loop belief propagation and foreground propagation are iteratively implemented for the minimization. We verify our method on ICoseg dataset. Six existing cosegmentation methods are used for the comparison. The experimental results demonstrate that the proposed method can not only improve MGC performance in terms of larger IOU values, but is also robust to the parameter setting.