Re-Ranking Images Retrieval: Combined Multiple Features Method
John Bosco P1, S.K.V.Jayakumar2
1JOHN BOSCO P*, Department of Computer Science and Engineering, Pondicherry University, Puducherry, India.
2S K V JAYAKUMAR, Department of Computer Science and Engineering, Pondicherry University, Puducherry, India.
Manuscript received on 15 August 2019. | Revised Manuscript received on 25 August 2019. | Manuscript published on 30 September 2019. | PP: 1099-1105 | Volume-8 Issue-3 September 2019 | Retrieval Number: C4252098319/19©BEIESP | DOI: 10.35940/ijrte.C4252.098319
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Content-Based Image Retrieval (CBIR) grown rapidly in multimedia field, image retrieval, pattern recognition, etc. CBIR provides an effective way of image search and retrieval from the pool image databases. Learning effective relevance measures plays a critical role in improving the performance of image retrieval systems. In this paper present a Combined multiple features method which is two key parameters (i) Feature extraction, (ii) Similarity metrics for content-based image retrieval method. Feature extraction and similarity metrics important role in Content-Based Image Retrieval. We define hybrid feature extraction and similarity method for finding the most similar images retrieved. Combined features extraction using the various image features. These papers explain some important distance metrics such as Euclidean distance and City block distance. The experiments are performed using the various kinds of databases such as WANG Database, Corel Dataset. The experimental result shows that the proposed method is proved more effective than existing methods.
Keywords: CBIR, Multiple Features, Feature Extraction, WANG Dataset.
Scope of the Article: Information Retrieval