A unified context assessing model for object categorization
Computer Vision and Image Understanding
Images as sets of locally weighted features
Computer Vision and Image Understanding
Categorization of multiple objects in a scene without semantic segmentation
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part I
Bag of spatio-visual words for context inference in scene classification
Pattern Recognition
Salient object detection via color contrast and color distribution
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part I
Visual Saliency with Statistical Priors
International Journal of Computer Vision
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The increased use of context for high level reasoning has been popular in recent works to increase recognition accuracy. In this paper, we consider an orthogonal application of context. We explore the use of context to determine which low-level appearance cues in an image are salient or representative of an image's contents. Existing classes of low-level saliency measures for image patches include those based on interest points, as well as supervised discriminative measures. We propose a new class of unsupervised contextual saliency measures based on co-occurrence and spatial information between image patches. For recognition, image patches are sampled using a weighted random sampling based on saliency, or using a sequential approach based on maximizing the likelihoods of the image patches. We compare the different classes of saliency measures, along with a baseline uniform measure, for the task of scene and object recognition using the bag-of-features paradigm. In our results, the contextual saliency measures achieve improved accuracies over the previous methods. Moreover, our highest accuracy is achieved using a sparse sampling of the image, unlike previous approaches who's performance increases with the sampling density.