Stochastic Optimized Relevance Feedback Particle Swarm Optimization for Content Based Image Retrieval
One of the major challenges for the CBIR is to bridge the gap between low level features and high level semantics according to the need of the user. To overcome this gap, relevance feedback (RF) coupled with support vector machine (SVM) has been applied successfully. However, when the feedback sampl...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2014-01-01
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| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2014/752090 |
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| author | Muhammad Imran Rathiah Hashim Abd Khalid Noor Elaiza Aun Irtaza |
| author_facet | Muhammad Imran Rathiah Hashim Abd Khalid Noor Elaiza Aun Irtaza |
| author_sort | Muhammad Imran |
| collection | DOAJ |
| description | One of the major challenges for the CBIR is to bridge the gap between low level features and high level semantics according to the need of the user. To overcome this gap, relevance feedback (RF) coupled with support vector machine (SVM) has been applied successfully. However, when the feedback sample is small, the performance of the SVM based RF is often poor. To improve the performance of RF, this paper has proposed a new technique, namely, PSO-SVM-RF, which combines SVM based RF with particle swarm optimization (PSO). The aims of this proposed technique are to enhance the performance of SVM based RF and also to minimize the user interaction with the system by minimizing the RF number. The PSO-SVM-RF was tested on the coral photo gallery containing 10908 images. The results obtained from the experiments showed that the proposed PSO-SVM-RF achieved 100% accuracy in 8 feedback iterations for top 10 retrievals and 80% accuracy in 6 iterations for 100 top retrievals. This implies that with PSO-SVM-RF technique high accuracy rate is achieved at a small number of iterations. |
| format | Article |
| id | doaj-art-0696889a2aa049848ea04edf26124faa |
| institution | Kabale University |
| issn | 2356-6140 1537-744X |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj-art-0696889a2aa049848ea04edf26124faa2025-02-03T06:06:25ZengWileyThe Scientific World Journal2356-61401537-744X2014-01-01201410.1155/2014/752090752090Stochastic Optimized Relevance Feedback Particle Swarm Optimization for Content Based Image RetrievalMuhammad Imran0Rathiah Hashim1Abd Khalid Noor Elaiza2Aun Irtaza3Universiti Tun Hussein Onn Malaysia (UTHM), Parit Raja, 86400 Batu Pahat, Johor, MalaysiaUniversiti Tun Hussein Onn Malaysia (UTHM), Parit Raja, 86400 Batu Pahat, Johor, MalaysiaUniversiti Teknologi MARA (UiTM), 40450 Shah Alam, Selangor Darul Ehsan, MalaysiaUniversity of Engineering and Technology, Taxila, PakistanOne of the major challenges for the CBIR is to bridge the gap between low level features and high level semantics according to the need of the user. To overcome this gap, relevance feedback (RF) coupled with support vector machine (SVM) has been applied successfully. However, when the feedback sample is small, the performance of the SVM based RF is often poor. To improve the performance of RF, this paper has proposed a new technique, namely, PSO-SVM-RF, which combines SVM based RF with particle swarm optimization (PSO). The aims of this proposed technique are to enhance the performance of SVM based RF and also to minimize the user interaction with the system by minimizing the RF number. The PSO-SVM-RF was tested on the coral photo gallery containing 10908 images. The results obtained from the experiments showed that the proposed PSO-SVM-RF achieved 100% accuracy in 8 feedback iterations for top 10 retrievals and 80% accuracy in 6 iterations for 100 top retrievals. This implies that with PSO-SVM-RF technique high accuracy rate is achieved at a small number of iterations.http://dx.doi.org/10.1155/2014/752090 |
| spellingShingle | Muhammad Imran Rathiah Hashim Abd Khalid Noor Elaiza Aun Irtaza Stochastic Optimized Relevance Feedback Particle Swarm Optimization for Content Based Image Retrieval The Scientific World Journal |
| title | Stochastic Optimized Relevance Feedback Particle Swarm Optimization for Content Based Image Retrieval |
| title_full | Stochastic Optimized Relevance Feedback Particle Swarm Optimization for Content Based Image Retrieval |
| title_fullStr | Stochastic Optimized Relevance Feedback Particle Swarm Optimization for Content Based Image Retrieval |
| title_full_unstemmed | Stochastic Optimized Relevance Feedback Particle Swarm Optimization for Content Based Image Retrieval |
| title_short | Stochastic Optimized Relevance Feedback Particle Swarm Optimization for Content Based Image Retrieval |
| title_sort | stochastic optimized relevance feedback particle swarm optimization for content based image retrieval |
| url | http://dx.doi.org/10.1155/2014/752090 |
| work_keys_str_mv | AT muhammadimran stochasticoptimizedrelevancefeedbackparticleswarmoptimizationforcontentbasedimageretrieval AT rathiahhashim stochasticoptimizedrelevancefeedbackparticleswarmoptimizationforcontentbasedimageretrieval AT abdkhalidnoorelaiza stochasticoptimizedrelevancefeedbackparticleswarmoptimizationforcontentbasedimageretrieval AT aunirtaza stochasticoptimizedrelevancefeedbackparticleswarmoptimizationforcontentbasedimageretrieval |