Squeezing the DCT to Fight Camouflage

Marcos Escudero-Viñolo, Jesús Bescós

Abstract: This paper presents a novel descriptor based on the two-dimensional discrete cosine transform (2D DCT) to fight camouflage. The 2D DCT gained popularity in image and video analysis owing to its wide use in signal compression. The 2D DCT is a well-established example to evaluate new techniques in sparse representation and is widely used for block and texture description, mainly due to its simplicity and its ability to condense information in a few coefficients. A common approach, for different applications, is to select a subset of these coefficients, which is fixed for every analyzed signal. In this paper, we question this approach and propose a novel method to select a signal-dependent subset of relevant coefficients, which is the basis for the proposed R-DCT and sR-DCT descriptors. As we propose to describe each pixel with a different set of coefficients, each associated to a particular basis function, in order to compare any two so-obtained descriptors a distance function is required: we propose a novel metric to cope with this situation. The presented experiments over the change detection dataset show that the proposed descriptors notably reduce the likelihood of camouflage respect to other popular descriptors: 92% respect to the pixel luminance, 82% respect to the RGB values, and 65% respect to the best performing LBP configuration.