Content based Image Retrieval using Deep Learning Technique with Distance Measures
M. Aswini Varalakshmi, Geetanjali Nayak
CBIR, CNN, Bag of visual words.,vgg16,vgg19
The combination of convolution neural networks (CNN) and deep learning generated a stunning result in a variety of image processing applications. For separating comparable images, CNN-based techniques to isolate image features from the last layer and the use of a single CNN structure could be used. The Content-Based Image Retrieval system is used to learn highlight extraction and efficient similarity examination (CBIR). Highlight extraction, like similarity tests, plays an important role in CBIR. The research is carried out using datasets such as the UC Merced Land Use Dataset. Using a pre-trained model that has been adjusted for the retrieval task and has been trained on a large number of photographs. For the retrieval process, pre-trained CNN models are used to generate image highlight descriptors. By using move learning and retrieval of highlight vectors using various similarity measures, this technique manages component extraction from the two completely connected layers present in the VGG-16 network. The proposed engineering has a fantastic presentation in terms of extracting features as well as learning features without any prior knowledge of the images. With vgg19 , we're going to be able to extend our work with CNN.Investigation of configuration and bunching for signs of improved execution. The outcomes were measured and execution correlation was completed using various execution metrics. In both completely connected layers, cosine similarity and Euclidean distance work better.
Article Details
Unique Paper ID: 153543

Publication Volume & Issue: Volume 8, Issue 8

Page(s): 398 - 404
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