— Context is critical for reducing the uncertainty in object detection. However, context modelling is challenging because there are often many different types of contextual infor...
—SIFT-like local feature descriptors are ubiquitously employed in such computer vision applications as content-based retrieval, video analysis, copy detection, object recognition...
Christoph Strecha, Alexander A. Bronstein, Michael...
— Art historians have long observed the highly characteristic brushstroke styles of Vincent van Gogh and have relied on discerning these styles for authenticating and dating his ...
—Cross-domain learning methods have shown promising results by leveraging labeled patterns from the auxiliary domain to learn a robust classifier for the target domain which has ...
—Locating the center of the eyes allows for valuable information to be captured and used in a wide range of applications. Accurate eye center location can be determined using com...
Abstract—We propose a robust fitting framework, called Adaptive Kernel-Scale Weighted Hypotheses (AKSWH), to segment multiplestructure data even in the presence of a large number...
—We describe an approach for segmenting a moving image into regions that correspond to surfaces in the scene that are partially surrounded by the medium. It integrates both appea...
—We present a novel framework to generate and rank plausible hypotheses for the spatial extent of objects in images using bottom-up computational processes and mid-level selectio...
—Social media networks contain both content and context-specific information. Most existing methods work with either of the two for the purpose of multimedia mining and retrieva...
Guo-Jun Qi, Charu C. Aggarwal, Qi Tian, Heng Ji, T...
—We propose an efficient and robust solution for image set classification. A joint representation of an image set is proposed which includes the image samples of the set and thei...