Paper:
Foreground Clustering for Joint Segmentation and Localization in Videos and Images
This paper presents a novel framework in which video/image segmentation and localization are cast into a single optimization problem that integrates information from low level appearance cues with that of high level localization cues in a very weakly supervised manner. The proposed framework leverages two representations at different levels, exploits the spatial relationship between bounding boxes and superpixels as linear constraints and simultaneously discriminates between foreground and background at bounding box and superpixel level. Different from previous approaches that mainly rely on discriminative clustering, we incorporate a foreground model that minimizes the histogram difference of an object across all image frames. Exploiting the geometric relation between the superpixels and bounding boxes enables the transfer of segmentation cues to improve localization output and vice-versa. Inclusion of the foreground model generalizes our discriminative framework to video data where the background tends to be similar and thus, not discriminative. We demonstrate the effectiveness of our unified framework on the YouTube Object video dataset, Internet Object Discovery dataset and Pascal VOC 2007.

Review:
The paper is good, presents a new idea that achieves excellent results at the level of performance. I'm familiar with these types of techniques but I can not quite understand why has been used the histogram metric rather than some gradient-based technique (e.g., Gradient Edge Detection) to differentiate an object across all image.  The submission present a video/image segmentation and localization framework for learns to localize and segment common objects in images and videos using a discriminative clustering model. The originality of the work is focused on: 1) Introduce a foreground/beckground discriminative model nased on histogram matching term. This foreground model are incorporated into discriminative clustering one and optimizated together. 3) Show a novel mechanism to exploit spatial relation between superpixel and bounding box in an unsupervised way.  The technical content of the paper appears to be correct but the authors did not highlight well both the strengths and weaknesses of their work.  It's generally well-written and structured clearly. Maybe it's too structured in the paragraph 6. In this case, I recommend making the text more fluid.   I don't have any minor remarks and typos.