Classification on Unsupervised Deep Hashing With Pseudo Labels Using Support Vector Machine for Scalable Image Retrieval

Classification on Unsupervised Deep Hashing With Pseudo Labels Using Support Vector Machine for Scalable Image Retrieval


  • Rohit Sharma Department of Electronics & Communication Engineering, SRM Institute of Science and Technology, NCR Campus, Ghaziabad, UP, India
  • Bipin Kumar Rai ABES Institute of Technology, Ghaziabad, India
  • Shubham Sharma Mechanical Engineering Department, University Center for Research & Development, Chandigarh University, Mohali, Punjab, India



CNN, Deep Learning, Image Retrieval, SVM


The content-based image retrieval (CBIR) method operates on the low-level visual features of the user input query object, which makes it difficult for users to formulate the query and also does not provide adequate retrieval results. In the past, image annotation was suggested as the best possible framework for CBIR, which works on automatically signing keywords to images that support image retrieval. The recent successes of deep learning techniques, especially Convolutional Neural Networks (CNN), in solving computer vision applications have inspired me to work on this paper to solve the problem of CBIR using a dataset of annotated images


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How to Cite

Sharma, R., Rai, B., & Sharma, S. (2023). Classification on Unsupervised Deep Hashing With Pseudo Labels Using Support Vector Machine for Scalable Image Retrieval . Wasit Journal of Computer and Mathematics Science, 2(2), 40–55.