Exploiting sparse representations in very high-dimensional feature spaces obtained from patch-based processing

Use of high-dimensional feature spaces in a system has standard problems that must be addressed such as the high calculation costs, storage demands, and training requirements. To partially circumvent this problem, we propose the conjunction of the very high-dimensional feature space and image patches. This union allows for the image patches to be efficiently represented as sparse vectors while taking advantage of the high-dimensional properties. The key to making the system perform efficiently is the use of a sparse histogram representation for the color space which makes the calculations largely independent of the feature space dimension. The system can operate under multiple L (p) norms or mixed metrics which allows for optimized metrics for the feature vector. An optimal tree structure is also introduced for the approximate nearest neighbor tree to aid in patch classification. It is shown that the system can be applied to various applications and used effectively.