Modified one-class support vector machine for content-based image retrieval with relevance feedback
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 Exa...
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doaj-d1d9711a06a84fc48abda51b957c4b2b2021-03-02T14:46:48ZengTaylor & Francis GroupCogent Engineering2331-19162018-01-015110.1080/23311916.2018.15417021541702Modified one-class support vector machine for content-based image retrieval with relevance feedbackOluwole A. Adegbola0David O. Aborisade1Segun I. Popoola2Olatide A. Amole3Aderemi A. Atayero4Ladoke Akintola University of TechnologyLadoke Akintola University of TechnologyCovenant UniversityBells University of TechnologyCovenant UniversityImage 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.http://dx.doi.org/10.1080/23311916.2018.1541702content-based image retrievalone-class support vector machinerelevance feedbackprincipal component analysisvisual descriptors |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Oluwole A. Adegbola David O. Aborisade Segun I. Popoola Olatide A. Amole Aderemi A. Atayero |
spellingShingle |
Oluwole A. Adegbola David O. Aborisade Segun I. Popoola Olatide A. Amole Aderemi A. Atayero Modified one-class support vector machine for content-based image retrieval with relevance feedback Cogent Engineering content-based image retrieval one-class support vector machine relevance feedback principal component analysis visual descriptors |
author_facet |
Oluwole A. Adegbola David O. Aborisade Segun I. Popoola Olatide A. Amole Aderemi A. Atayero |
author_sort |
Oluwole A. Adegbola |
title |
Modified one-class support vector machine for content-based image retrieval with relevance feedback |
title_short |
Modified one-class support vector machine for content-based image retrieval with relevance feedback |
title_full |
Modified one-class support vector machine for content-based image retrieval with relevance feedback |
title_fullStr |
Modified one-class support vector machine for content-based image retrieval with relevance feedback |
title_full_unstemmed |
Modified one-class support vector machine for content-based image retrieval with relevance feedback |
title_sort |
modified one-class support vector machine for content-based image retrieval with relevance feedback |
publisher |
Taylor & Francis Group |
series |
Cogent Engineering |
issn |
2331-1916 |
publishDate |
2018-01-01 |
description |
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. |
topic |
content-based image retrieval one-class support vector machine relevance feedback principal component analysis visual descriptors |
url |
http://dx.doi.org/10.1080/23311916.2018.1541702 |
work_keys_str_mv |
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