Cross-domain learning methods have shown promising results by leveraging labeled
patterns from auxiliary domains to learn a robust classifier for target domain, which has a …

Many multimedia applications can benefit from techniques for adapting existing classifiers to
data with different distributions. One example is cross-domain video concept detection which …

Exploding amounts of multimedia data increasingly require automatic indexing and
classification, eg training classifiers to produce high-level features, or semantic concepts …

We propose a multiple source domain adaptation method, referred to as Domain Adaptation
Machine (DAM), to learn a robust decision function (referred to as target classifier) for label …

In this paper, an ontology-based multi-classification learning algorithm is adopted to detect
concepts in the NIST TREC-2003 video retrieval benchmark which defines 133 video …

Video concept learning often requires a large set of training samples. In practice, however,
acquiring noise-free training labels with sufficient positive examples is very expensive. A …

We present a compositional model for video event detection. A video is modeled using a
collection of both global and segment-level features and kernel functions are employed for …

For large scale automatic semantic video characterization, it is necessary to learn and model
a large number of semantic concepts. But a major obstacle to this is the insufficiency of …

Video object detection is a fundamental tool for many applications. Since direct application
of image-based object detection cannot leverage the rich temporal information inherent in …

Automatic semantic concept detection in video is important for effective content-based video
retrieval and mining and has gained great attention recently. In this paper, we propose a …