Adaptive tree similarity learning for image retrieval

2003 ◽  
Vol 9 (2) ◽  
pp. 131-143 ◽  
Author(s):  
Tao Wang ◽  
Yong Rui ◽  
Shi-min Hu ◽  
Jia-guang Sun
Keyword(s):  
Image Retrieval ◽  
Similarity Learning ◽  
Tree Similarity

scholarly journals Semisupervised Online Multikernel Similarity Learning for Image Retrieval

2017 ◽  
Vol 19 (5) ◽  
pp. 1077-1089 ◽  
Author(s):  
Jianqing Liang ◽  
Qinghua Hu ◽  
Wenwu Wang ◽  
Yahong Han
Keyword(s):  
Image Retrieval ◽  
Similarity Learning

Image Retrieval Based on Similarity Learning

2014 ◽  
Vol 9 (3) ◽  
Author(s):  
Zhaohui Liu ◽  
Rongfu Zhou
Keyword(s):  
Image Retrieval ◽  
Similarity Learning

scholarly journals Bit-Scalable Deep Hashing With Regularized Similarity Learning for Image Retrieval and Person Re-Identification

2015 ◽  
Vol 24 (12) ◽  
pp. 4766-4779 ◽  
Author(s):  
Ruimao Zhang ◽  
Liang Lin ◽  
Rui Zhang ◽  
Wangmeng Zuo ◽  
Lei Zhang
Keyword(s):  
Image Retrieval ◽  
Similarity Learning ◽  
Deep Hashing

A Novel Image Retrieval System Based on Histogram Factorization and Contextual Similarity Learning

2013 ◽  
Vol 380-384 ◽  
pp. 4148-4151 ◽  
Author(s):  
Sivakolundu Jayasekara ◽  
Hithanadura Dassanayake ◽  
Anil Fernando
Keyword(s):  
Image Retrieval ◽  
Matrix Factorization ◽  
Large Scale ◽  
Nonnegative Matrix Factorization ◽  
Retrieval System ◽  
Nonnegative Matrix ◽  
Bag Of Words ◽  
Similarity Learning ◽  
Query Image ◽  
Image Retrieval System

Image retrieval has been a top topic in the field of both computer vision and machine learning for a long time. Content based image retrieval, which tries to retrieve images from a database visually similar to a query image, has attracted much attention. Two most important issues of image retrieval are the representation and ranking of the images. Recently, bag-of-words based method has shown its power as a representation method. Moreover, nonnegative matrix factorization is also a popular way to represent the data samples. In addition, contextual similarity learning has also been studied and proven to be an effective method for the ranking problem. However, these technologies have never been used together. In this paper, we developed an effective image retrieval system by representing each image using the bag-of-words method as histograms, and then apply the nonnegative matrix factorization to factorize the histograms, and finally learn the ranking score using the contextual similarity learning method. The proposed novel system is evaluated on a large scale image database and the effectiveness is shown.

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