A discriminative learning technique for mobile landmark recognition

This paper proposes a discriminative learning bags-of-words (BoW) approach for mobile landmark recognition at patch and image levels. Conventional methods often treat the local patches and images equally important for recognition and do not differentiate their different importance. Although there exist several works that consider the patches' discrimination information, they mainly focus on which patches are to be retained for training and do not incorporate this information when generating the BoW histograms. In view of this, this paper proposes to learn the discriminative information for each landmark category at two levels: local patches and images. At patch level, the patches' discrimination information for each landmark is first discovered using an iterative learning approach. This information is then incorporated into the quantization process to generate the BoW histogram. At image level, the different importance of training images is estimated through a non-parametric density estimator. Finally, fuzzy SVM is used to train the classifier for each category. Experimental results on a landmark database consisting of 3622 training images and 534 testing images show that the proposed method is effective in mobile landmark recognition.