Computer Vision and Machine Learning especially in the problems related to sparse representation and multi-image cosegmentation.
We introduced the novel notion of deconstructive learning and proposed a practical computational framework for deconstructing a broad class of binary classifiers commonly used in computer vision applications. While the ultimate objective of most learning problems is the determination of classifiers from labeled training data, for deconstructive learning, the objects of study are the classifiers themselves.
As its name suggests, the goal of deconstructive learning is to deconstruct a given classifier by determining and characterizing (as much as possible) the full extent of its capability, revealing all of its powers, subtleties and limitations. In particular, this problem is motivated by the seemingly innocuous question that given an image-based binary classifier C as a black-box oracle, how much can we learn of its internal working by simply querying it?
We presented We have worked on deconstructing Support Vector Machines and Cascade of Binary Classifiers.