Supervised Hashing for Image Retrieval via Image Represention Learning-笔记1

最新推荐文章于 2022-03-29 15:30:24 发布

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最新推荐文章于 2022-03-29 15:30:24 发布

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分类专栏：图像检索深度学习 caffe系列学习

本文链接：https://blog.youkuaiyun.com/qq_25073253/article/details/53914796

本文提出了一种监督哈希方法，用于图像检索，通过自动学习适合哈希的图像表示和一组哈希函数。该方法分为两阶段，首先将相似性矩阵分解为哈希代码矩阵H，然后使用深度卷积网络学习图像的特征表示和哈希函数，以增强哈希性能。

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摘要

Background：

In the existing supervised hashing methods for images ,an input image is usually encoded by a vector of hand-crafted visual features.

e.g. Such hand-crafted feature vectors do not necessarily preserve the accurate semantic similarities of images pairs,which may often degrade the performance of hashing function learning.（人工提取的特征无法保证图片对之间的语义正确性，也会降低哈希函数学习的性能）

In this paper：

We propose a supervised hashing method for image retrieval, in which we automatically learn a good image representation tailored to hashing as well as a set of hash functions.The proposed method has two stages. In the first stage, given the pairwise similarity matrix S over training images, we propose a scalable coordinate descent method to decompose S into a product of HHT where H is a matrix with each of its rows being the approximate hash code associated to a training image. In the second stage, we propose to simultaneously learn a good feature representation for the input images as well as a set of hash functions, via a deep convolutional network tailored to the learned hash codes in H and optionally the discrete class labels of the images.

Introduction

The learning-based hashing methods can be divided into three main streams.

<a>Unsupervised methonds,in which only unlabeled data is used to learn hash functions.（无监督）

<b>The other two streams are semi-supervised and supervised methods.（半监督和监督）

Key question:

In learning-based hashing for images is how to encode images into a useful feature representation so as to enhance the hashing performance.

Ideally,one would like to automatically learn such a fecture representation that sufficiently preserves the semantic similarities for images during the hash learning process.

e.g. Without using hand-crafted visual features, Semantic Hashing (Salakhutdinov and Hinton 2007) is a hashing method which automatically constructs binary-code feature representation for images by a multi-layer auto-encoder, with the raw pixels of images being directly used as input.

Semantic hashing imposes