The University of Surrey Visual Concept Detection System at ImageCLEF 2010: Working Notes
Tahir, A, Yan, F, Barnard, M, Awais, M, Mikolajczyk, K and Kittler, J (2010) The University of Surrey Visual Concept Detection System at ImageCLEF 2010: Working Notes In: ICPR 2010, 2010-08-23 - 2010-08-26, Istanbul, Turkey.
Available under License : See the attached licence file.
Visual concept detection is one of the most important tasks in image and video indexing. This paper describes our system in the ImageCLEF@ICPR Visual Concept Detection Task which ranked first for large-scale visual concept detection tasks in terms of Equal Error Rate (EER) and Area under Curve (AUC) and ranked third in terms of hierarchical measure. The presented approach involves state-of-the-art local descriptor computation, vector quantisation via clustering, structured scene or object representation via localised histograms of vector codes, similarity measure for kernel construction and classifier learning. The main novelty is the classifier-level and kernel-level fusion using Kernel Discriminant Analysis with RBF/Power Chi-Squared kernels obtained from various image descriptors. For 32 out of 53 individual concepts, we obtain the best performance of all 12 submissions to this task.
|Item Type:||Conference or Workshop Item (Conference Paper)|
|Divisions :||Faculty of Engineering and Physical Sciences > Electronic Engineering > Centre for Vision Speech and Signal Processing|
|Identification Number :||https://doi.org/10.1007/978-3-642-17711-8_17|
|Additional Information :||
Copyright 2010 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
|Depositing User :||Symplectic Elements|
|Date Deposited :||14 Dec 2012 09:35|
|Last Modified :||23 Sep 2013 19:47|
Actions (login required)
Downloads per month over past year