Object Level Deep Feature Pooling for Compact Image Representation

Mopuri, Konda Reddy and Babu, Venkatesh R (2015) Object Level Deep Feature Pooling for Compact Image Representation. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), JUN 07-12, 2015, Boston, MA.

PDF IEEE_Con_Com_Vis_Pat_Rec_Wor_62_2015.pdf - Published Version Restricted to Registered users only Download (711kB) | Request a copy

Abstract

Convolutional Neural Network (CNN) features have been successfully employed in recent works as an image descriptor for various vision tasks. But the inability of the deep CNN features to exhibit invariance to geometric transformations and object compositions poses a great challenge for image search. In this work, we demonstrate the effectiveness of the objectness prior over the deep CNN features of image regions for obtaining an invariant image representation. The proposed approach represents the image as a vector of pooled CNN features describing the underlying objects. This representation provides robustness to spatial layout of the objects in the scene and achieves invariance to general geometric transformations, such as translation, rotation and scaling. The proposed approach also leads to a compact representation of the scene, making each image occupy a smaller memory footprint. Experiments show that the proposed representation achieves state of the art retrieval results on a set of challenging benchmark image datasets, while maintaining a compact representation.

Item Type:	Conference Proceedings
Series.:	IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
Additional Information:	Copy right for this article belongs to the IEEE, 345 E 47TH ST, NEW YORK, NY 10017 USA
Department/Centre:	Division of Interdisciplinary Sciences > Supercomputer Education & Research Centre
Date Deposited:	19 Aug 2016 06:07
Last Modified:	19 Aug 2016 06:07
URI:	http://eprints.iisc.ac.in/id/eprint/54385

Actions (login required)

View Item