We have developed a unique system for image authentication and recovery. Content-based invisible watermarks are designed based on the invariant properties of such lossy compression and embedded into reconstructable coefficients. Applications include medical, insurance, law enforcement, and news, in which some manipulations are required while malicious manipulations must be rejected. We are investigating innovative techniques using integrated multimedia features for automatic image classification. This is a collaborative project with the Natural Language Processing group at Columbia.
Demos show automatic classification using combined text-visual features.
We develop a novel interactive system for learning visual object filters, in which models are defined by a user according to his interests via a multiple-level object definition hierarchy. The system facilitates cooperation between user and system, in which the computer performs automatic image region segmentation while the user manually labels and maps segmented regions to various nodes in the object definition hierarchy. As the user provides examples from images or video, Visual Object Detectors are constructed automatically using a variety of machine learning techniques. We introduce the concept of Recurrent Visual Semantics and show how it can be exploited in our framework. Our approach is applied in the context of baseball videos and the Kosovo crisis for filtering.