A Framework for the Image Retrieval System Based on Histogram Normalization Technique with Python
Madhuri Pydi1, K.L.Sailaja2
1Madhuri pydi*, from Computer Science and Engineering, VR Siddhartha Engineering College, Vijayawada, India.
2K.L.Sailaja, Department of CSE, from VR Siddhartha Engineering College, Vijayawada, India.
Manuscript received on April 01, 2020. | Revised Manuscript received on April 14, 2020. | Manuscript published on April 30, 2020. | PP: 2300-2301 | Volume-9 Issue-4, April 2020. | Retrieval Number: D9060049420/2020©BEIESP | DOI: 10.35940/ijeat.D9060.049420
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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 processing of multimedia content is used for real-world computer vision in various applications, and digital images make up a large part of multimedia data. The processing of multimedia content is used for real-computer vision in various applications, and digital images make up a large part of multimedia data. Content-based Retrieval of photographs (CBIR) is a system of picture recovery which utilizes the visual highlights of a picture, for example, shading, shape and surface so as to look through the client based inquiry pictures from the huge databases. CBIR relies upon highlight extraction of a picture which are the visual highlights and these highlights are extricated naturally i.e. without human collaboration. We intend in this paper to provide a detailed overview of recent developments related to CBIR and image representation. We researched the main aspects of various models of image recovery and image representation from low-level feature extraction to recent semantict ML approaches. And, for extraction of features, HSV, image segmentation and color histogram techniques are used, which effectively gives us the main point in an image that these techniques are used to minimize complexity, expense, and energy and time consumption. Then a machine learning model is trained for similarity test and the validation and texting phases are performed accordingly which leads to better performance as. Then a machine learning model is trained for similarity testing and then the validation and texting steps are performed accordingly, resulting in improved results compared to previously performed techniques. The precision values in the proposed technique are fairly excellent.
Keywords: CBIR, Feature Extraction, HSV, Image segmentation, Multimedia.