Image Retrieval System using Residual Neural Network in a Distributed Environment
R Rajkumar1, M V Sudhamani2

1Mr. R Rajkumar*, Research Scholar-VTU, Asst. Professor, Information Science & Engg., RNS Institute of Technology, Bengaluru, India.
2Dr.M.V.Sudhamani Professor & HoD, Department of Information Science & Engg., RNS Institute of Technology, Bengaluru, India.
Manuscript received on March 15, 2020. | Revised Manuscript received on March 24, 2020. | Manuscript published on March 30, 2020. | PP: 4597-4605 | Volume-8 Issue-6, March 2020. | Retrieval Number: F9811038620/2020©BEIESP | DOI: 10.35940/ijrte.F9811.038620

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Abstract: Development of Content-Based Image Retrieval systems supports retrieval of similar images based on selected features. Selection of appropriate features for this process is a difficult task. In this regard, deep learning concept helps in choosing appropriate features for retrieval. In this work, Content-Based Image Retrieval system is proposed using Convolution Neural Network known as Residual Neural Network model. The dataset used to build retrieval system is collection of web images 50,000 of 250 categories. The model is trained on 40% of image data and tested on 60% of data. When user submits a query image from the client-side, similar features are extracted by the model on server-side. Later, the features of query image are compared with trained images data and similarity is measured using the metric of Euclidean distance. The retrieved resultant images are displayed on Graphical User Interface. The results are comparatively higher with the existing systems. The proposed work is also compared with Google’s Image retrieval system for random query images and our proposed work has shown a better performance by 14.27%.
Keywords: Content-Based Image Retrieval, Residual Network, Convolution Neural Network, Euclidean distance.
Scope of the Article: Smart learning methods and environments.