Efficient Evaluation of SVM Classifiers Using Error Space Encoding

Many computer vision tasks require efficient evaluation of Support Vector Machine (SVM) classifiers on large image databases. Our goal is to efficiently evaluate SVM classifiers on a large number of images. We propose a novel Error Space Encoding (ESE) scheme for SVM evaluation which utilizes large number of classifiers already evaluated on the similar data set. We model this problem as an encoding of a novel classifier (query) in terms of the existing classifiers (query logs). With sufficiently large query logs, we show that ESE performs far better than any other existing encoding schemes. With this method we are able to retrieve nearly 100% correct top-k images from a dataset of 1 Million images spanning across 1000 categories. We also demonstrate application of our method in terms of relevance feedback and query expansion mechanism and show that our method achieves the same accuracy 90 times faster than exhaustive SVM evaluations.