Multiple Instance Learning (MIL) is a special kind of supervised learning problem that has been studied actively in recent years. We propose an approach based on One-Class Support ...
Discriminative learning techniques for sequential data have proven to be more effective than generative models for named entity recognition, information extraction, and other task...
This paper presents a technique to enable deformable regions to be matched using image databases based on the information provided by the differential invariants of local histogram...
Image retrieval and image compression have been pursued separately in the past. Only little research has been conducted on a synthesis of the two by allowing image retrieval to be ...
We propose a new fast facial-feature extraction technique for embedded face-recognition applications. A deformable feature model is adopted, of which the parameters are optimized t...