In this paper we present a segmentation proposal method which employs a box-hypotheses generation step followed by a lightweight segmentation strategy. Inspired by interactive segmentation, for each automatically placed bounding-box we compute a precise segmentation mask. We introduce diversity in segmentation strategies enhancing a generic model performance exploiting class-independent regional appearance features. Foreground probability scores are learned from groups of objects with peculiar characteristics to specialize segmentation models. We demonstrate results comparable to the state-of-the-art on PASCAL VOC 2012 and a further improvement by merging our proposals with those of a recent solution. The ability to generalize to unseen object categories is demonstrated on Microsoft COCO 2014.

Segmentation models diversity for object proposals / Manfredi, Marco; Grana, Costantino; Cucchiara, Rita; Smeulders, Arnold W. M.. - In: COMPUTER VISION AND IMAGE UNDERSTANDING. - ISSN 1077-3142. - STAMPA. - 158(2017), pp. 40-48.

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