Top row to bottom: Input images, Results of our saliency algorithm, Ground truth labeling.

Salient object detection has become an important task in many image processing applications. The existing approaches exploit background prior and contrast prior to attain state of the art results. In this paper, instead of using background cues, we estimate the foreground regions in an image using objectness proposals and utilize it to obtain smooth and accurate saliency maps. We propose a novel saliency measure called ‘foreground connectivity’ which determines how tightly a pixel or a region is connected to the estimated foreground. We use the values assigned by this measure as foreground weights and integrate these in an optimization framework to obtain the final saliency maps. We extensively evaluate the proposed approach on two benchmark databases and demonstrate that the results obtained are better than the existing state of the art approaches.

Sai Srivatsa R, R Venkatesh Babu. "Salient Object Detection via Objectness Measure".
IEEE International Conference on Image Processing (IEEE ICIP), Quebec city, Canada 2015.
Paper   |   BibTeX   |   Poster   |   Code

Visual Comparison of our Saliency Map with other state-of-the-art methods.Our approach produces smoother,uniform and accurate Saliency Maps



Comparison of Precision-Recall curves of
various approaches on MSRA Dataset

Comparison of Precision-Recall curves of
various approaches on CSSD Dataset

Comparison of Mean Absolute Error of
various approaches on MSRA Dataset

Comparison of Mean Absolute Error of
various approaches on CSSD Dataset

[1] Ming-Ming Cheng, Ziming Zhang, Wen-Yan Lin, and Philip H. S. Torr, “BING: Binarized normed gradients for objectness estimation at 300fps,” in IEEE CVPR, 2014.
[2] Federico Perazzi, Philipp Krähenbül , Yael Pritch, and Alexander Hornung, “Saliency filters: Contrast based filtering for salient region detection,” in IEEE CVPR 2012
[3] Wangjiang Zhu, Yichen Wei, Fang Wen and Jian Sun, “Geodesic saliency using background priors,” in ECCV 2012.
[4] Huchuan Lu, Xiang Ruan Chuan Yang, Lihe Zhang and Ming-Hsuan Yang, “Saliency detection via graph-based manifold ranking,” in IEEE CVPR 2013.
[5] Yichen Wei, Wangjiang Zhu, Shuang Liangy and Jian Sun, “Saliency optimization from robust background detection,” in IEEE CVPR 2014.