Content based Retrieval Management Systems in Web Engineering
Prameela Devi. Chillakuru1, T. Kumanan2, C H. Sarada Devi3
1Prameela Devi. Chillakuru, Research Scholar, Meenashi Academy of Higher Education and Research, Chennai (Tamil Nadu), India.
2Dr. T. Kumanan, Professor, Department of CSE, Meenashi Academy of Higher Education and Research, Chennai (Tamil Nadu), India.
3C H. Sarada Devi, Research Scholar, Meenashi Academy of Higher Education and Research, Chennai (Tamil Nadu), India.
Manuscript received on 10 October 2019 | Revised Manuscript received on 19 October 2019 | Manuscript Published on 02 November 2019 | PP: 81-93 | Volume-8 Issue-2S11 September 2019 | Retrieval Number: B10140982S1119/2019©BEIESP | DOI: 10.35940/ijrte.B1014.0982S1119
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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: The tremendous upsurge and abundant availability of graphic documents on the internet directed to the high concern in research on Content-Based Image Retrieval (CBIR). It has cemented the attitude for a massive sum of innovative procedures and schemes, and growing curiosity in respective fields to preserve such research. Existing technologies were discussed about various CBIR techniques such as k-means, content-based image, and hybrid clustering, etc. in combination with the feature vector of information images and texture features. In similar cases, it is constricted in stating the user’s semantic knowledge to permit information distribution and reuse. Hence models ought to be managed within repositories, where they might be retrieved upon users’ queries. Due to the lack of adequate tools on incisive/handling for visual content, this research work proposes an Efficient Density-based Clustering Algorithm (EDBC) for CBIR technique that will enhance scalability, reduce the user search time, and lower the maintenance cost. Using the existing CBIR, the color, texture, and shape features of an image is identified by integrating with the proposed EDBC algorithm and thereby, it improves the scalability and user search time. When comparing the feature between the color histograms of RGB and CMYK, it shows better color characteristics in CMYK by using the proposed technique. Also, the grouping of color, shape and texture features based image retrieval improves the accuracy when compared with existing methods.
Keywords: CBIR, EDBC, Clustering, Support Vector Machine (SVM), RGB, CMYK, Colour Histograms, Texture and Shape, Feature Extraction.
Scope of the Article: Web Mining