Selecting representative and distinctive descriptors for efficient landmark recognition

To have a robust and informative image content representation for image categorization, we often need to extract as many as possible visual features at various locations, scales and orientations. Thus it is not surprised that an image has a few hundreds or even thousands of visual descriptors. This raises huge cost of computation and memory. To eliminate the problem, we can only select the most representative and distinctive descriptors and discard the other non-informative features when training the image category models. This paper will present a Markov chain based algorithm to learn a measure of the descriptor importance in order to weigh the degree of representativeness and distinctiveness. From the measures the descriptor selection algorithm is derived. The presented approach starts from constructing a graph with each node being a descriptor to characterize the pair-wise descriptor similarity and then the PageRank algorithm is exploited to estimate the stationary distribution of the graph whose values are the indicator of the descriptor importance. We evaluate the proposed approach on the STOIC-101 landmark dataset. Our experiments demonstrate the Markov chain based descriptor selection can select the most informative descriptors to distinguish the landmarks. Even with the large reduction of the size of descriptors, the classification accuracy is still competitive or overcomes compared with the system without any descriptor selection.