A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient Graph-Based Image Segmentation
International Journal of Computer Vision
"GrabCut": interactive foreground extraction using iterated graph cuts
ACM SIGGRAPH 2004 Papers
2006 Special Issue: Modeling attention to salient proto-objects
Neural Networks
Real-time bag of words, approximately
Proceedings of the ACM International Conference on Image and Video Retrieval
Evaluating Color Descriptors for Object and Scene Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Measuring and Predicting Object Importance
International Journal of Computer Vision
Segmentation as selective search for object recognition
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
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In this paper we propose a novel approach to the task of salient object detection. In contrast to previous salient object detectors that are based on a spotlight attention theory, we follow an object-based attention theory and incorporate the notion of an object directly into our saliency measurements. Particularly, we consider proto-objects as units of the analysis, where a proto-object is a connected image region that can be converted into a plausible object or object-part, once a focus of attention reaches it. As the object-based attention theory suggests, we start with segmenting a complex image into proto-objects and then assess saliency for each proto-object. The most salient proto-object is considered as being a salient object. We distinguish two types of object saliency. Firstly, an object is salient if it differs from its surrounding, which we call center-surround saliency. Secondly, an object is salient if it contains rare or outstanding details, which we measure by integrated saliency. We demonstrate that these two types of object saliency have complementary characteristics; moreover, the combination of the two performs at the level of state-of-the-art in salient object detection.