Content-based video retrieval and indexing have been associated with intelligent methods
in many applications such as education, medicine and agriculture. However, an extensive …

Emerging Internet services and applications attract increasing users to involve in diverse
video-related activities, such as video searching, video downloading, video sharing and so …

In this paper we introduce ViSiL, a Video Similarity Learning architecture that considers fine-
grained Spatio-Temporal relations between pairs of videos--such relations are typically lost …

Learning-based hashing methods have attracted considerable attention due to their ability to
greatly increase the scale at which existing algorithms may operate. Most of these methods …

Hash-based methods achieve fast similarity search by representing high-dimensional data
with compact binary codes. However, both generating binary codes and encoding unseen …

Near-duplicate video retrieval (NDVR) has been a significant research task in multimedia
given its high impact in applications, such as video search, recommendation, and copyright …

This work addresses the problem of Near-Duplicate Video Retrieval (NDVR). We propose
an efficient video-level NDVR scheme based on deep metric learning that leverages CNN …

With the exponential growth of web multimedia contents, the Internet is rife with near-
duplicate videos, the video copies applied with visual/temporal transformations and/or post …

Abstract The problem of Near-Duplicate Video Retrieval (NDVR) has attracted increasing
interest due to the huge growth of video content on the Web, which is characterized by high …

With the explosive growth of video data in real applications, near-duplicate video retrieval
(NDVR) has become indispensable and challenging, especially for short videos. However …