Fast content based image retrieval using convolutional neural network and hash function
Due to the explosive increase of online images, content based image retrieval has gained a lot of attention. The success of deep learning techniques such as convolutional neural networks have motivated us to explore its applications in our context. The main contribution of our work is a novel end to end supervised learning framework that learns probability based semantic level similarity and feature level similarity simultaneously. The main advantage of our novel hashing scheme that it is able to reduce the computational cost of retrieval significantly at the state of the art efficiency level. We report on comprehensive experiments using public available datasets such as Oxford, Holidays and ImageNet 2012 retrieval datasets.
2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC)