PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

On a Quest for Image Descriptors Based on Unsupervised Segmentation Maps
Piotr Koniusz and Krystian Mikolajczyk
In: ICPR 2010, Istanbul(2010).


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.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Machine Vision
ID Code:8095
Deposited By:Piotr Koniusz
Deposited On:18 April 2011