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Support Vector Machines (SVMs) are powerful learners that have led tostate-of-the-art results in various computer vision problems. SVMs suffer fromvarious drawbacks in terms of selecting the right kernel, which depends on theimage descriptors, as well as computational and memory efficiency. This paperintroduces a novel kernel, which serves such issues well. The kernel is learnedby exploiting a large amount of low-complex, randomized binary mappings of theinput feature. This leads to an efficient SVM, while also alleviating the taskof kernel selection. We demonstrate the capabilities of our kernel on 6standard vision benchmarks, in which we combine several common imagedescriptors, namely histograms (Flowers17 and Daimler), attribute-likedescriptors (UCI, OSR, and a-VOC08), and Sparse Quantization (ImageNet).Results show that our kernel learning adapts well to the different descriptorstypes, achieving the performance of the kernels specifically tuned for eachimage descriptor, and with similar evaluation cost as efficient SVM methods.

Random Binary Mappings for Kernel Learning and Efficient SVM