PERFORMANCE OF A COMPACT FEATURE VECTOR IN CONTENT-BASED IMAGE RETRIEVAL

Gita Das, Sid Ray

Abstract

In this paper, we considered image retrieval as a dichotomous classification problem and studied the effect of sample size and dimensionality on the retrieval accuracy. Finite sample size has always been a problem in Content-Based Image Retrieval (CBIR) system and it is more severe when feature dimension is high. Here, we have discussed feature vectors having different dimensions and their performance with real and synthetic data, with varying sample sizes. We reported experimental results and analysis with two different image databases of size 1000, each with 10 semantic categories.

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Paper Citation


in Harvard Style

Das G. and Ray S. (2007). PERFORMANCE OF A COMPACT FEATURE VECTOR IN CONTENT-BASED IMAGE RETRIEVAL . In Proceedings of the Second International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, ISBN 978-972-8865-74-0, pages 241-246. DOI: 10.5220/0002046102410246


in Bibtex Style

@conference{visapp07,
author={Gita Das and Sid Ray},
title={PERFORMANCE OF A COMPACT FEATURE VECTOR IN CONTENT-BASED IMAGE RETRIEVAL},
booktitle={Proceedings of the Second International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP,},
year={2007},
pages={241-246},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002046102410246},
isbn={978-972-8865-74-0},
}


in EndNote Style

TY - CONF
JO - Proceedings of the Second International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP,
TI - PERFORMANCE OF A COMPACT FEATURE VECTOR IN CONTENT-BASED IMAGE RETRIEVAL
SN - 978-972-8865-74-0
AU - Das G.
AU - Ray S.
PY - 2007
SP - 241
EP - 246
DO - 10.5220/0002046102410246