Abstract

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 im- age 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 pro- pose a new class of unsupervised contextual saliency measures based on co-occurrence and spatial information between image patches. For recog- nition, 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 ob ject 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.