Authors:
Gita Das
and
Sid Ray
Affiliation:
Clayton School of Information Technology, Monash University, Australia
Keyword(s):
CBIR, feature representation, sample size, dimensionality.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Data Manipulation
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Methodologies and Methods
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Soft Computing
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.