Image Retrieval using Histogram Factorization and Contextual Similarity Learning

TL;DR

This paper presents a novel image retrieval system that combines bag-of-words histograms, nonnegative matrix factorization, and contextual similarity learning to improve image ranking accuracy.

Contribution

It introduces a new integrated approach that combines existing techniques in a unique way for enhanced image retrieval performance.

Findings

Effective retrieval demonstrated on large-scale database

Improved ranking accuracy over traditional methods

Combines multiple techniques for better representation and ranking

Abstract

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.