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Modified one-class support vector machine for content-based image retrieval with relevance feedback
- Source :
- Cogent Engineering, Vol 5, Iss 1 (2018)
- Publication Year :
- 2018
- Publisher :
- Taylor & Francis Group, 2018.
-
Abstract
- Image retrieval via traditional Content-Based Image Retrieval (CBIR) often incurs the semantic gap problem—non-correlation of image retrieval results with human semantic interpretation of images. In this paper, Relevance Feedback (RF) mechanism was incorporated into a traditional Query by Visual Example CBIR (QVER) system. The inherent curse of dimensionality associated with RF mechanism was catered for by performing feature selection using Principal Component Analysis (PCA). The amount of feature dimension retained was determined based on a not more than 5% loss constrain imposed on average precision of retrieval result. While the asymmetry and small sample size nature of the resultant image dataset informed the use of a modified One-Class Support Vector Machine (OC-SVM) classifier, three image databases (DB10, DB20 and DB100) were used to test the OC-SVM RF mechanism. Across DB10, DB20 and DB100, Average Indexing Time of 0.451, 0.3017, and 0.0904s were recorded, respectively. For a critical recall value of 0.3, precision values for QVER were 0.7881, 0.7200 and 0.9112, while OC-SVM RF yielded precision of 0.8908, 0.8409, and 0.9503, respectively. Also, the use of PCA yielded tolerable degradation of 3.54, 4.39 and 7.40% in precision on DB10, DB20, and DB100, respectively, with 80% reduction in feature dimension. The OC-SVM RF increased the precision and invariably the reliability of the CBIR system by ranking most of the relevant images higher. Also, the target class was identified faster than the conventional method, thereby reducing the image retrieval time of the OC-SVM RF.
- Subjects :
- General Computer Science
Computer science
principal component analysis
General Chemical Engineering
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
Relevance feedback
Feature selection
02 engineering and technology
Content-based image retrieval
content-based image retrieval
0202 electrical engineering, electronic engineering, information engineering
Image retrieval
visual descriptors
relevance feedback
business.industry
Search engine indexing
General Engineering
020207 software engineering
Pattern recognition
one-class support vector machine
Support vector machine
lcsh:TA1-2040
020201 artificial intelligence & image processing
Artificial intelligence
business
lcsh:Engineering (General). Civil engineering (General)
Semantic gap
Curse of dimensionality
Subjects
Details
- Language :
- English
- ISSN :
- 23311916
- Volume :
- 5
- Issue :
- 1
- Database :
- OpenAIRE
- Journal :
- Cogent Engineering
- Accession number :
- edsair.doi.dedup.....16f09c97596be05dfbcc50601d53b154