Abstract--This paper proposes a no-reference quality assessment metric for digital video subject to H.264/AVC encoding. The proposed metric comprises two main steps: coding error e...
This paper presents a supervised manifold learning model for dimensionality reduction in image and video classification tasks. Unlike most manifold learning models that emphasize ...
In this paper, we model multi-agent events in terms of a temporally varying sequence of sub-events, and propose a novel approach for learning, detecting and representing events in...
We introduce a new large-scale video dataset designed to assess the performance of diverse visual event recognition algorithms with a focus on continuous visual event recognition ...
Sangmin Oh, Anthony Hoogs, A.G.Amitha Perera, Chia...
We present a generic, efficient and iterative algorithm for interactively clustering classes of images and videos. The approach moves away from the use of large hand labelled tra...