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This project forked from yinglang/imageretrival

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以图查图,52各类,每类24张图,使用了cnn提特征和传统的sift bow结合svm几种组合方式

MATLAB 25.27% M 0.02% C++ 5.91% Makefile 2.43% TeX 3.76% Python 4.62% CSS 1.10% JavaScript 0.02% HTML 4.84% Cuda 8.27% C 41.62% Shell 0.78% Clean 0.20% Objective-C 0.83% Roff 0.33%

imageretrival's Introduction

目录 说明
code 使用的算法的实现代码
data 裁剪后的图片数据
third_part_lib 依赖的第三方库
third_part_lib\vl_feature 用于提取sift
third_part_lib\matconvtnet 一个CNN的matlab库,用于加载解析cnn model

code 中的算法 与 程序入口的对应关系

	方法                | 对应的代码位置

--------------------|-------------------------------------------------------------------------- sift bow + 余弦 | code/cv_bow/bow_pipeline.m sift bow + svm | code/cv_bow/bow_svm_pipeline.m color feature + 余弦 | code/cv_bow/color_feature_pipeline.m cnn feature + 余弦 | code/cv_cnn/cnn_query/pipeline.m cnn feature + svm | code/cv_cnn/cnn_svm/pipeline.m cnn feature + 余弦 + rerank | code/cv_cnn/rerank/pipeline.m cnn feature + svd + svm | code/cv_cnn/cnn_svm/pipeline.m (修改35行为 para.useSVD = 1; ) 纯 sift 匹配 | code/cv_sift_dir/pipeline.m scsm | 效果不理想,没有把代码加进来

该代码使用的数据云盘链接:http://pan.baidu.com/s/1cgdpjo
该代码对该数据集提取的特征: http://pan.baidu.com/s/1nvlg50t 该特征不是运行代码所必需的,但是如果使用提取好的特征可以省去其中的提取特征和聚类的时间(大概3小时)

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