基于伪标签的无监督深度哈希分类支持向量机可扩展图像检索

Rohit Sharma, Bipin Kumar Rai, Shubham Sharma
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引用次数: 0

摘要

基于内容的图像检索(CBIR)方法是基于用户输入查询对象的底层视觉特征进行操作的,这使得用户难以制定查询,也不能提供足够的检索结果。过去,图像注释被认为是CBIR的最佳框架,它可以自动将关键字签名到支持图像检索的图像上。最近深度学习技术,特别是卷积神经网络(CNN)在解决计算机视觉应用方面的成功,激发了我在这篇论文中使用带注释的图像数据集来解决CBIR问题
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Classification on Unsupervised Deep Hashing With Pseudo Labels Using Support Vector Machine for Scalable Image Retrieval
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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