University of Surrey

Test tubes in the lab Research in the ATI Dance Research

On a quest for image descriptors based on unsupervised segmentation maps

Koniusz, P and Mikolajczyk, K (2010) On a quest for image descriptors based on unsupervised segmentation maps In: 2010 20th ICPR, 2010-08-23 - 2010-08-26, Istanbul, Turkey.

[img] Text
05596040.pdf - ["content_typename_UNSPECIFIED" not defined]
Restricted to Repository staff only
Available under License : See the attached licence file.

Download (681kB)
[img] PDF (licence)
SRI_deposit_agreement.pdf
Restricted to Repository staff only
Available under License : See the attached licence file.

Download (33kB)

Abstract

This paper investigates segmentation-based image descriptors for object category recognition. In contrast to commonly used interest points the proposed descriptors are extracted from pairs of adjacent regions given by a segmentation method. In this way we exploit semi-local structural information from the image. We propose to use the segments as spatial bins for descriptors of various image statistics based on gradient, colour and region shape. Proposed descriptors are validated on standard recognition benchmarks. Results show they outperform state-of-the-art reference descriptors with 5.6x less data and achieve comparable results to them with 8.6x less data. The proposed descriptors are complementary to SIFT and achieve state-of-the-art results when combined together within a kernel based classifier.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Authors :
AuthorsEmailORCID
Koniusz, PUNSPECIFIEDUNSPECIFIED
Mikolajczyk, KUNSPECIFIEDUNSPECIFIED
Date : 2010
Identification Number : https://doi.org/10.1109/ICPR.2010.192
Depositing User : Symplectic Elements
Date Deposited : 28 Mar 2017 13:12
Last Modified : 28 Mar 2017 13:12
URI: http://epubs.surrey.ac.uk/id/eprint/806165

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year


Information about this web site

© The University of Surrey, Guildford, Surrey, GU2 7XH, United Kingdom.
+44 (0)1483 300800