To cite this record: http://ddd.uab.cat/record/56433
A Performance Evaluation of Exact and Approximate Match Kernels for Object Recognition
Caputo and Luo Jie, Barbara

Date: 2009
Abstract: Local features have repeatedly shown their effectiveness for object recognition during the last years, and they have consequently become the preferred descriptor for this type of problems. The solution of the correspondence problem is traditionally approached with exact or approximate techniques. In this paper we are interested in methods that solve the correspondence problem via the definition of a kernel function that makes it possible to use local features as input to a support vector machine. We single out the match kernel, an exact approach, and the pyramid match kernel, that uses instead an approximate strategy. We present a thorough experimental evaluation of the two methods on three different databases. Results show that the exact method performs consistently better than the approximate one, especially for the object identification task, when training on a decreasing number of images. Based on these findings and on the computational cost of each approach, we suggest some criteria for choosing between the two kernels given the application at hand.
Rights: Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial i la comunicació pública de l'obra, sempre que no sigui amb finalitats comercials, i sempre que es reconegui l'autoria de l'obra original. No es permet la creació d'obres derivades. Creative Commons
Language: Anglès.
Document: article ; recerca ; publishedVersion
Subject: Object recognition ; Local features ; Kernel methods
Published in: ELCVIA : Electronic Letters on Computer Vision and Image Analysis, Vol. 8, Núm. 3 ( 2009) , p. 15-26, ISSN 1577-5097



12 p, 2.6 MB

The record appears in these collections:
Articles > Published articles > ELCVIA : Electronic Letters on Computer Vision and Image Analysis

 Record created 2010-05-12, last modified 2014-06-07



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