Saliency Segmentation based on Learning and Graph Cut Refinement

Saliency detection is a well researched problem in computer vision. In previous work, most of the effort is spent on manually devising a saliency measure. Instead we propose a simple algorithm that uses a dataset with manually marked salient objects to learn to detect saliency. Building on the recent success of segmentation-based approaches to object detection, our saliency detection is based on image superpixels, as opposed to individual image pixels. Our features are the standard ones often used in vision, i.e. they are based on color, texture, etc. These simple features, properly normalized, surprisingly have a performance superior to the methods with hand-crafted features specifically designed for saliency detection. We refine the initial segmentation returned by the learned classifier by performing binary graph-cut optimization. This refinement step is performed on pixel level to alleviate any potential inaccuracies due to superpixel tesselation. The initial appearance models are...

• Type: Journal
• Year: 2010
• Where: BMVC
• Authors: Paria Mehrani, Olga Veksler
• Keywords: Binary Graph-cut Optimization, BMVC 2010, Computer Vision, Iterative Segmentation Framework, Saliency Detection