Statistical Framework for Effective Retrieval of Images based on Content
Subash Chandra Chadalavada1, Srinivas Yarramalle2
1Subash Chandra Chadalavada, Assoc. Prof., Department of CSE, Kakinada Institute of Engineering & Technology, Korangi (A.P), India.
2Srinivas Yarramalle, Professor, Department of IT, GITAM Institute of Technology, GITAM University, Vizag (A.P), India.
Manuscript received on 18 April 2019 | Revised Manuscript received on 25 April 2019 | Manuscript published on 30 April 2019 | PP: 872-876 | Volume-8 Issue-4, April 2019 | Retrieval Number: D6480048419/19©BEIESP
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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 new digital technology updates have resulted in huge image capture capabilities using multiple resolution techniques. However, this has led to a disadvantage with respect to storage and recovery efficiencies. To address this problem, content-based image retrieval (CBIR) has been coined and has become the core consideration for the effective recovery of massive data sets. With recovery competencies, CBIR has been used in many applications ranging from medical processing, video and audio recovery and recognition of old documents. In this article, we present a recovery model based on the Bivariable Gamma Blending Model with a perspective on the application of video retrievals from YouTube video conferences considered as a data source. The main advantage of this model is that the recovery of a relevant conference / video clip can be easily recovered, so that users can have their choice of reference within a very short duration. The efficiency of the model is tested using benchmark quality metrics such as the signal-to-noise ratio (SNR), the mean square error (MSE) and the structural similarity index (SSIR) ratio
Keywords: Content Based Image Retrieval, Generalized Gamma Mixture Model, Signal to Noise Ratio, Mean Square Error, Structural Similarity Index Ratio
Scope of the Article: Image Processing