scholarly journals Content-Based Image Retrieval Using Customized Convolutional Neural Network

2020 ◽  
Vol 13 (14) ◽  
pp. 467-470
Author(s):  
A.P. Nilawar
Keyword(s):  
Neural Network ◽  
Image Retrieval ◽  
Convolutional Neural Network ◽  
Content Based Image Retrieval

scholarly journals A Novel Multi Hidden Layer Convolutional Neural Network for Content Based Image Retrieval

2020 ◽  
Vol 9 (3) ◽  
pp. 365-370
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Image Retrieval ◽  
Convolutional Neural Network ◽  
Network Architecture ◽  
Learning Algorithm ◽  
Machine Learning Algorithms ◽  
Content Based Image Retrieval ◽  
Neural Network Architecture ◽  
Query Image

The applications of a content-based image retrieval system in fields such as multimedia, security, medicine, and entertainment, have been implemented on a huge real-time database by using a convolutional neural network architecture. In general, thus far, content-based image retrieval systems have been implemented with machine learning algorithms. A machine learning algorithm is applicable to a limited database because of the few feature extraction hidden layers between the input and the output layers. The proposed convolutional neural network architecture was successfully implemented using 128 convolutional layers, pooling layers, rectifier linear unit (ReLu), and fully connected layers. A convolutional neural network architecture yields better results of its ability to extract features from an image. The Euclidean distance metric is used for calculating the similarity between the query image and the database images. It is implemented using the COREL database. The proposed system is successfully evaluated using precision, recall, and F-score. The performance of the proposed method is evaluated using the precision and recall.

Deep learning for fast bronze inscription image retrieval

2020 ◽  
Vol 4 (4) ◽  
pp. 291-296
Author(s):  
Ziyang Wang ◽  
Wei Zheng ◽  
Youguang Chen
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Image Retrieval ◽  
Convolutional Neural Network ◽  
Local Features ◽  
Content Based Image Retrieval ◽  
Query Image ◽  
Euclidean Metric

Collections of bronze inscription images are increasing rapidly. To use these images efficiently, we proposed an effective content-based image retrieval framework using deep learning. Specifically, we extract discriminative local features for image retrieval using the activations of the convolutional neural network and binarize the extracted features for improving the efficiency of image retrieval, firstly. Then, we use the cosine metric and Euclidean metric to calculate the similarity between the query image and dataset images. The result shows that the proposed framework has an impressive accuracy.

scholarly journals Image Retrieval based Convolutional Neural Network

2020 ◽  
Vol 31 (4) ◽  
pp. 43
Author(s):  
Nuha Mohammed Khassaf ◽  
Shaimaa Hameed Shaker
Keyword(s):  
Neural Network ◽  
Image Retrieval ◽  
Convolutional Neural Network ◽  
Experimental Evaluation ◽  
Texture Features ◽  
Content Based Image Retrieval ◽  
Large Set ◽  
Agricultural Field ◽  
Query Image ◽  
Efficient Retrieval

At the present time, everyone is interested in dealing with images in different fields such as geographic maps, medical images, images obtaining by Camera, microscope, telescope, agricultural field photos, paintings, industrial parts drawings, space photos, etc. Content Based Image Retrieval (CBIR) is an efficient retrieval of relevant images from databases based on features extracted from the image. Follow the proposed system for retrieving images related to a query image from a large set of images, based approach to extract the texture features present in the image using statistical methods (PCA, MAD, GLCM, and Fusion) after pre-processing of images. The proposed system was trained using 1D CNN using a dataset Corel10k which widely used for experimental evaluation of CBIR performance the results of proposed system shows that the highest accuracy is 97.5% using Fusion (PCA, MAD), where the accuracy is 95% using MAD, 90% using PCA. The performance result is acceptable compared to previous work.

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