Determining Patch Saliency Using Low-Level Context View Full Text


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

2008

AUTHORS

Devi Parikh , C. Lawrence Zitnick , Tsuhan Chen

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 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. More... »

PAGES

446-459

Book

TITLE

Computer Vision – ECCV 2008

ISBN

978-3-540-88685-3
978-3-540-88688-4

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-540-88688-4_33

DOI

http://dx.doi.org/10.1007/978-3-540-88688-4_33

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1044659023


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