Modified one-class support vector machine for content-based image retrieval with relevance feedback
| Autor: | Olatide A. Amole, Aderemi A. Atayero, Oluwole A. Adegbola, David O. Aborisade, Segun I. Popoola |
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| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
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 |
| Zdroj: | Cogent Engineering, Vol 5, Iss 1 (2018) |
| ISSN: | 2331-1916 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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