Local learning for classification is useful in dealing with various vision problems. One key factor for such approaches to be effective is to find good neighbors for the learning procedure. In this work, we describe a novel method to rank neighbors by learning a local distance function, and meanwhile to derive the local distance function by focusing on the high-ranked neighbors. The two aspects of considerations can be elegantly coupled through a well-defined objective function, motivated by a supervised ranking method called P-Norm Push. While the local distance functions are learned independently, they can be reshaped altogether so that their values can be directly compared. We apply the proposed method to the Caltech-101 dataset, and demonstrate the use of proper neighbors can improve the performance of classification techniques based on nearest-neighbor selection.

We address the tone reproduction problem by integrating local adaptation with global-contrast consistency. Many previous works have tried to compress high-dynamic-range (HDR) luminances into a displayable range in imitation of the local adaptation mechanism of human eyes. Nevertheless, while the realization of local adaptation is not theoretically defined, exaggerating such effects often causes unnatural global contrasts. We propose a luminance-driven perceptual grouping process to derive a sparse representation of HDR luminances, and use the grouped regions to approximate local properties of luminances. The advantage of incorporating a sparse representation is twofold: We can simulate local adaptation based on region information, and subsequently apply piecewise tone mappings to monotonize the relative brightness over only a few perceptually significant regions. Our experimental results show that the proposed framework gives a good balance in preserving local details and maintaining global contrasts of HDR scenes.