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This paper presents a novel framework in which video/image segmentation andlocalization are cast into a single optimization problem that integratesinformation from low level appearance cues with that of high level localizationcues in a very weakly supervised manner. The proposed framework leverages tworepresentations at different levels, exploits the spatial relationship betweenbounding boxes and superpixels as linear constraints and simultaneouslydiscriminates between foreground and background at bounding box and superpixellevel. Different from previous approaches that mainly rely on discriminativeclustering, we incorporate a foreground model that minimizes the histogramdifference of an object across all image frames. Exploiting the geometricrelation between the superpixels and bounding boxes enables the transfer ofsegmentation cues to improve localization output and vice-versa. Inclusion ofthe foreground model generalizes our discriminative framework to video datawhere the background tends to be similar and thus, not discriminative. Wedemonstrate the effectiveness of our unified framework on the YouTube Objectvideo dataset, Internet Object Discovery dataset and Pascal VOC 2007.

Foreground Clustering for Joint Segmentation and Localization in Videos and Images