Multimodal machine learning (MML) is a tempting multidisciplinary research area where
heterogeneous data from multiple modalities and machine learning (ML) are combined to …

We present the first comprehensive, open source multimedia knowledge extraction system
that takes a massive stream of unstructured, heterogeneous multimedia data from various …

We present a new information extraction system that can automatically construct temporal
event graphs from a collection of news documents from multiple sources, multiple languages …

Nowadays, web users frequently explore multimedia contents to satisfy their information
needs. The exploration approaches usually provide linear interaction mechanisms and do …

Video is a rich information source containing both audio and visual information along with
motion information embedded in it. Applications such as e-learning, live TV, video on …

Multimedia data encompasses various modalities, including audio, visual, and text,
necessitating the development of robust retrieval methods capable of harnessing these …

The heterogeneity of today's Web sources requires information retrieval (IR) systems to
handle multi-modal queries. Such queries define a user's information needs by different data …

Effective information retrieval (IR) relies on the ability to comprehensively capture a user's
information needs. Traditional IR systems are limited to homogeneous queries that define …

In the present article, we present an algorithm of content based video retrieval using Frame
fusion and Histogram of Oriented Gradients (HOG). Representative frames of database …

Purpose Consumers often encounter issues of perceived ambiguity and performance risk
when attempting to evaluate experience goods being offered online. Sellers try to alleviate …