We propose a novel unsupervised learning framework for activity perception. To understand activities in complicated scenes from visual data, we propose a hierarchical Bayesian mod...
We address the problem of detecting irregularities in visual data, e.g., detecting suspicious behaviors in video sequences, or identifying salient patterns in images. The term &qu...
Motivated by the need to efficiently leverage user relevance feedback in content-based retrieval from image databases, we propose a fast, clustering-based indexing technique for e...
This paper presents a method to infer hidden semantic cues by accumulating the knowledge learned from relevance feedback sessions. We propose to explicitly represent a semantic sp...
In many image or video retrieval systems, the search of similar objects in the database includes a spatial access method to a multidimensional feature space. This step is generall...