An Extreme Learning Machine-Relevance Feedback Framework for Enhancing the Accuracy of a Hybrid Image Retrieval System
The process of searching, indexing and retrieving images from a massive database is a challenging task and the solution to these problems is an efficient image retrieval system. In this paper, a unique hybrid Content-based image retrieval system is proposed where different attributes of an image lik...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Universidad Internacional de La Rioja (UNIR)
2020-06-01
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| Series: | International Journal of Interactive Multimedia and Artificial Intelligence |
| Subjects: | |
| Online Access: | http://www.ijimai.org/journal/bibcite/reference/2749 |
| _version_ | 1811331883477237760 |
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| author | B. Shikha P. Gitanjali D. Pawan Kumar |
| author_facet | B. Shikha P. Gitanjali D. Pawan Kumar |
| author_sort | B. Shikha |
| collection | DOAJ |
| description | The process of searching, indexing and retrieving images from a massive database is a challenging task and the solution to these problems is an efficient image retrieval system. In this paper, a unique hybrid Content-based image retrieval system is proposed where different attributes of an image like texture, color and shape are extracted by using Gray level co-occurrence matrix (GLCM), color moment and various region props procedure respectively. A hybrid feature matrix or vector (HFV) is formed by an integration of feature vectors belonging to three individual visual attributes. This HFV is given as an input to an Extreme learning machine (ELM) classifier which is based on a solitary hidden layer of neurons and also is a type of feed-forward neural system. ELM performs efficient class prediction of the query image based on the pre-trained data. Lastly, to capture the high level human semantic information, Relevance feedback (RF) is utilized to retrain or reformulate the training of ELM. The advantage of the proposed system is that a combination of an ELM-RF framework leads to an evolution of a modified learning and intelligent classification system. To measure the efficiency of the proposed system, various parameters like Precision, Recall and Accuracy are evaluated. Average precision of 93.05%, 81.03%, 75.8% and 90.14% is obtained respectively on Corel-1K, Corel-5K, Corel-10K and GHIM-10 benchmark datasets. The experimental analysis portrays that the implemented technique outmatches many state-of-the-art related approaches depicting varied hybrid CBIR system. |
| first_indexed | 2024-04-13T16:28:32Z |
| format | Article |
| id | doaj.art-37c20be6ca9c4f7e924ef7320db829c5 |
| institution | Directory Open Access Journal |
| issn | 1989-1660 1989-1660 |
| language | English |
| last_indexed | 2024-04-13T16:28:32Z |
| publishDate | 2020-06-01 |
| publisher | Universidad Internacional de La Rioja (UNIR) |
| record_format | Article |
| series | International Journal of Interactive Multimedia and Artificial Intelligence |
| spelling | doaj.art-37c20be6ca9c4f7e924ef7320db829c52022-12-22T02:39:41ZengUniversidad Internacional de La Rioja (UNIR)International Journal of Interactive Multimedia and Artificial Intelligence1989-16601989-16602020-06-01621310.9781/ijimai.2020.01.002ijimai.2020.01.002An Extreme Learning Machine-Relevance Feedback Framework for Enhancing the Accuracy of a Hybrid Image Retrieval SystemB. ShikhaP. GitanjaliD. Pawan KumarThe process of searching, indexing and retrieving images from a massive database is a challenging task and the solution to these problems is an efficient image retrieval system. In this paper, a unique hybrid Content-based image retrieval system is proposed where different attributes of an image like texture, color and shape are extracted by using Gray level co-occurrence matrix (GLCM), color moment and various region props procedure respectively. A hybrid feature matrix or vector (HFV) is formed by an integration of feature vectors belonging to three individual visual attributes. This HFV is given as an input to an Extreme learning machine (ELM) classifier which is based on a solitary hidden layer of neurons and also is a type of feed-forward neural system. ELM performs efficient class prediction of the query image based on the pre-trained data. Lastly, to capture the high level human semantic information, Relevance feedback (RF) is utilized to retrain or reformulate the training of ELM. The advantage of the proposed system is that a combination of an ELM-RF framework leads to an evolution of a modified learning and intelligent classification system. To measure the efficiency of the proposed system, various parameters like Precision, Recall and Accuracy are evaluated. Average precision of 93.05%, 81.03%, 75.8% and 90.14% is obtained respectively on Corel-1K, Corel-5K, Corel-10K and GHIM-10 benchmark datasets. The experimental analysis portrays that the implemented technique outmatches many state-of-the-art related approaches depicting varied hybrid CBIR system.http://www.ijimai.org/journal/bibcite/reference/2749extreme learning machinegray level co-occurrence matrixrelevance feedbackregion props procedure |
| spellingShingle | B. Shikha P. Gitanjali D. Pawan Kumar An Extreme Learning Machine-Relevance Feedback Framework for Enhancing the Accuracy of a Hybrid Image Retrieval System International Journal of Interactive Multimedia and Artificial Intelligence extreme learning machine gray level co-occurrence matrix relevance feedback region props procedure |
| title | An Extreme Learning Machine-Relevance Feedback Framework for Enhancing the Accuracy of a Hybrid Image Retrieval System |
| title_full | An Extreme Learning Machine-Relevance Feedback Framework for Enhancing the Accuracy of a Hybrid Image Retrieval System |
| title_fullStr | An Extreme Learning Machine-Relevance Feedback Framework for Enhancing the Accuracy of a Hybrid Image Retrieval System |
| title_full_unstemmed | An Extreme Learning Machine-Relevance Feedback Framework for Enhancing the Accuracy of a Hybrid Image Retrieval System |
| title_short | An Extreme Learning Machine-Relevance Feedback Framework for Enhancing the Accuracy of a Hybrid Image Retrieval System |
| title_sort | extreme learning machine relevance feedback framework for enhancing the accuracy of a hybrid image retrieval system |
| topic | extreme learning machine gray level co-occurrence matrix relevance feedback region props procedure |
| url | http://www.ijimai.org/journal/bibcite/reference/2749 |
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