https://github.com/kevinlin311tw/caffe-cvprw15 这是一篇比较简单的论文,就简单说下思路即可 Deep Learning of Binary Hash Codes for Fast Image Retrieval 就是直接使用CNN模型的7层结果作为特征,但是直接计算两个4096维的向量是十分不高效的,提出使用PCA和判别维度缩减方法来压缩特征 传统的线性搜索方法已经不适...
Deep Learning of Binary Hash Codes for Fast Image Retrieval 这篇文章发表在2015CVPR workshop 文章链接:cv-foundation.org/opena 代码链接:github.com/kevinlin311t 图一 算法框架流程 这篇文章的想法很巧妙,在一个深层CNN的最后一个全连接层(fc8)和倒数第二个全连接层(fc7)之间加了一层全连接隐层,就是...
Deep Learning of Binary Hash Codes for Fast Image Retrieval (2015CVPR) 文章链接:http://www.cv-foundation.org/openaccess/content_cvpr_workshops_2015/W03/papers/Lin_Deep_Learning_of_2015_CVPR_paper.pdf 代码链接:https://github.com/kevinlin311tw/caffe-cvprw15 图一 算法框架流程 这篇文章的想法很...
Siamese网络在metric learning (Song et al, 2016), dimensionality reduction (Hadsell et al, 2006), learning image descriptors (SimoSerra et al, 2015), and performing face identification (Chopra et al, 2005; Hu et al, 2014; Sun et al, 2014)中很好地实现了。最近,triplet网络(即three-stream s...
Deep Image Retrieval a Survey 前言 基于内容的图像检索 (CBIR) 是在给定描述用户需求的情况下查询图像,通过分析视觉内容(其实还可能存在其它模态,例如文本描述)在大型图像库中搜索语义匹配或相似图像的问题,如下图 CBIR 可以大致分为实例级检索(instance level retrieval)和类别级检索(category level retrieval),如下...
learning of the deep features of the images and the construction of the Hash function are completed in the same process, an internal relation of the image features and the Hash function is explored, and the accuracy rate of the image retrieval is greatly increased; quantization error loss is ...
3.2 Image Retrieval via Hierarchical Deep Search Zeiler和Fergus研究过CNN的浅层学习局部视觉表示,高层捕捉语义信息能更好地用于识别。采用由粗到细的搜索策略以满足图片检索的速度和精度。首先通过相似的高层语义检索出一系列的候选图片,他们在隐层H上具有相似的二进制激活;随后进一步筛选在中层特征上具有相似性的图片...
Deep Learning of Binary Hash Codes for Fast Image Retrieval [Paper] [Code-Caffe] 1. 摘要 针对图像检索问题,提出简单有效的监督学习框架 CNN网络结构能同时学习图像特征表示以及 hash-like 编码函数集合 利用深度学习以逐点(point-wise)的方式,得到二值哈希编码(binary hash codes),以快速检索图像;对比卷积pair...
[18] is a representation that encodes by the residual vectors with respect to a dictionary, and Fisher Vector [30] can be formulated as a probabilistic version [18] of VLAD. Both of them are powerful shallow representations for image retrieval and classification [4, 47]. For vector ...
"Deep Image Retrieval: Learning global representations for image search" by Diane Larlus While deep learning has become a key ingredient in the top performing methods for many computer vision tasks, it has failed so far to bring similar improve... G Hutilisateurs 被引量: 0发表: 2017年 [Lec...