Random Binary Mappings for Kernel Learning and Efficient SVM
This addresses kernel selection and efficiency issues for SVM users in computer vision, though it is incremental as it builds on existing SVM methods.
The paper tackled the problem of kernel selection and computational efficiency in Support Vector Machines (SVMs) for computer vision by introducing a novel kernel learned from randomized binary mappings, achieving performance comparable to specifically tuned kernels on 6 standard benchmarks.
Support Vector Machines (SVMs) are powerful learners that have led to state-of-the-art results in various computer vision problems. SVMs suffer from various drawbacks in terms of selecting the right kernel, which depends on the image descriptors, as well as computational and memory efficiency. This paper introduces a novel kernel, which serves such issues well. The kernel is learned by exploiting a large amount of low-complex, randomized binary mappings of the input feature. This leads to an efficient SVM, while also alleviating the task of kernel selection. We demonstrate the capabilities of our kernel on 6 standard vision benchmarks, in which we combine several common image descriptors, namely histograms (Flowers17 and Daimler), attribute-like descriptors (UCI, OSR, and a-VOC08), and Sparse Quantization (ImageNet). Results show that our kernel learning adapts well to the different descriptors types, achieving the performance of the kernels specifically tuned for each image descriptor, and with similar evaluation cost as efficient SVM methods.