哈希图像检索方法研究
哈希图像检索方法研究(任务书,开题报告,论文12000字)
摘要
随着全球互联网技术的飞速发展，越来越多的图像被上传到互联网，每个人每天接触到的图片数量迅速增长，人们对于图像检索的要求也越来越高。为了解决大规模图像检索问题，普遍采用基于哈希的图像检索算法，然而检索的准确率还是无法让人们满意。如何提高哈希图像检索方法的准确率是一个难题，本文针对这一问题展开了研究。
近年来哈希算法在图像检索领域得到了越来越广泛的运用。本文在传统迭代量化哈希方法与多特征哈希方法的基础之上提出了一种多特征迭代哈希图像检索方法。传统方法采用的单一特征不能完整的表达一幅图像的内容信息，本文提出采用多特征迭代哈希方法来实现大规模图像的快速检索。本文提出的多特征迭代哈希方法通过学习数据的几个特征上的紧凑哈希码，同时考虑到了不同特征对应哈希码的关系，最后通过迭代量化的方法得到最优的哈希码，将检索结果按相似度从大到小依次排列。
本文在公开的CIFAR-10数据集上对该算法进行了实验，实验证明该方法检索准确率优于迭代量化哈希、位置敏感哈希、移不变核位置敏感哈希这三种单特征哈希方法。
关键词：图像检索；哈希；多特征；迭代量化
Abstract
With the rapid development of global Internet technology, more and more images are uploaded to the Internet, and the number of pictures that each person encounters each day increases rapidly, and people's requirements for image retrieval are also increasing. In order to solve the problem of large-scale image retrieval, hash-based image retrieval algorithms are commonly used. However, the accuracy of retrieval cannot satisfy people. How to improve the accuracy of the hash image retrieval method is a difficult problem. This article focuses on this issue.
In recent years, the hash algorithm has been more and more widely used in the field of image retrieval. This paper proposes a multi-feature iterative hash image retrieval method based on the traditional iterative quantification hash method and multi-feature hash method. The single feature adopted by the traditional method can not completely express the content information of an image. This paper proposes to use a multi-feature iterative hashing method to achieve fast retrieval of large-scale images. The multi-feature iterative hashing method proposed in this paper learns the compact hash code on several features of the data, taking into account the relationship between different features and the corresponding hash code. Finally, an iterative quantization method is used to obtain the optimal hash code. The search results are ranked in descending order of similarity. [资料来源：http://Doc163.com]
In this paper, the algorithm is tested on the published CIFAR-10 data set. Experiments show that this method has better retrieval accuracy than iteratively quantized hash, position-sensitive hash, and shift-invariant position-sensitive hash. Greek method.
Key Words：Image Retrieval；Hashing；Multi-Future；Iterative quantization;
[资料来源：https://www.doc163.com]
目录
摘要 I
Abstract II
第1章绪论 1
1.1研究背景及意义 1
1.2发展现状及问题 2
1.3本文主要内容及结构 3
第2章哈希图像检索相关知识 5
2.1图像检索基础知识 5
2.1.1基于文本的图像检索 5
2.1.2基于内容的图像检索 6
2.2图像的特征提取 7
2.2.1颜色特征 7
2.2.2纹理特征 7
2.2.3形状特征 8
2.3图像哈希算法 8
2.3.1位置敏感哈希（LSH） 9
2.3.2谱哈希（SH） 10
2.3.3锚点图哈希（AGH） 10 [来源：http://www.doc163.com]
2.4本章小结 11
第3章多特征迭代哈希方法设计与实现 12
3.1迭代量化哈希（ITQ） 12
3.2符号与标记 13
3.3目标函数 13
3.4迭代求解 14
3.5本章小结 15
第4章实验与分析 16
4.1实验环境及参数 16
4.2实验性能评价 16
4.3实验结果分析 17
4.4本章小结 18
第5章总结与展望 19
5.1总结 19
5.2展望 19
参考文献 20
致谢 22 [资料来源：http://doc163.com]