scholarly journals Content Based Image Retrieval from Auto Encoders Using Keras and Tensor Flow Python API a Deep Learning Technique

2020 ◽  
Vol 5 (9) ◽  
pp. 869-871
Author(s):  
Vinitha V ◽  
Velantina . V
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Image Retrieval ◽  
Retrieval System ◽  
Content Based Image Retrieval ◽  
New Methods ◽  
Methods And Techniques ◽  
Learning Technique ◽  
Image Retrieval System

As the technology is evolving new methods and techniques are determined and implemented in a smart way to improve and achieve a greater insight in this smart era. The retrieval of image is popularly growing in this emerging trend. In this paper we have used how to build a very simple image retrieval system using a special type of Neural Network called auto encoders. Here the images can be retrieved with visual contents textures, shape and this method of image retrieval is called content based image retrieval.

Hyperbolic Hopfield neural networks for image classification in content-based image retrieval

2020 ◽  
pp. 2050059
Author(s):  
K. Anitha ◽  
R. Dhanalakshmi ◽  
K. Naresh ◽  
D. Rukmani Devi
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Image Retrieval ◽  
Image Classification ◽  
Retrieval System ◽  
Activation Function ◽  
Hopfield Neural Networks ◽  
Content Based Image Retrieval ◽  
Image Retrieval System ◽  
Complex Valued

Neural networks play a significant role in data classification. Complex-valued Hopfield Neural Network (CHNN) is mostly used in various fields including the image classification. Though CHNN has proven its credibility in the classification task, it has a few issues. Activation function of complex-valued neuron maps to a unit circle in the complex plane affecting the resolution factor, flexibility and compatibility to changes, during adaptation in retrieval systems. The proposed work demonstrates Content-Based Image Retrieval System (CBIR) with Hyperbolic Hopfield Neural Networks (HHNN), an analogue of CHNN for classifying images. Activation function of the Hyperbolic neuron is not cyclic in hyperbolic plane. The images are mathematically represented and indexed using the six basic features. The proposed HHNN classifier is trained, tested and evaluated through extensive experiments considering individual features and four combined features for indexing. The obtained results prove that HHNN guides retrieval process, enhances system performance and minimizes the cost of implementing Neural Network Classifier-based image retrieval system.

scholarly journals Image Retrieval System using Residual Neural Network in a Distributed Environment

2020 ◽  
Vol 8 (6) ◽  
pp. 4597-4605
Keyword(s):  
Neural Network ◽  
Image Retrieval ◽  
Retrieval System ◽  
Image Data ◽  
Content Based Image Retrieval ◽  
Features Selection ◽  
Query Image ◽  
Server Side ◽  
Image Retrieval System ◽  
Client Side

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%.

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