  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.
