Salient Object Detection Based on the Proto-objects and Background Prior

Image salient object detection is useful for applications like image retrieval, object recognition etc. Aiming to obtain the pixel-level saliency map, most traditional saliency models ignore the object-level information and become in- appropriate in complex scenes. A new salient object detection method is proposed in this paper by incorporating the no- tion of object and background prior directly into the saliency measurement. Firstly the collection of proto-objects is ob- tained by adapting selective search method. Secondly for each proto-object, we measure its saliency score by computing the distance of histogram feature between the proto-object and the prior background region, then the saliency scores are ranked, and the top-ranked proto-object is considered as the salient object. The publicly MSRA dataset is adapted for ex- periment evaluation, compared with several other state-of-the-art methods, the proposed method produces superior per- formance. Experimental results show that the proposed method is effective.

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Salient Object Detection Based on the Proto-objects and Background Prior

Semantic Scholar · Computer Science · 2015

Abstract

Image salient object detection is useful for applications like image retrieval, object recognition etc. Aiming to obtain the pixel-level saliency map, most traditional saliency models ignore the object-level information and become in- appropriate in complex scenes. A new salient object detection method is proposed in this paper by incorporating the no- tion of object and background prior directly into the saliency measurement. Firstly the collection of proto-objects is ob- tained by adapting selective search method. Secondly for each proto-object, we measure its saliency score by computing the distance of histogram feature between the proto-object and the prior background region, then the saliency scores are ranked, and the top-ranked proto-object is considered as the salient object. The publicly MSRA dataset is adapted for ex- periment evaluation, compared with several other state-of-the-art methods, the proposed method produces superior per- formance. Experimental results show that the proposed method is effective.

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