In the early days, content-based image retrieval (CBIR) was studied with global features.
Since 2003, image retrieval based on local descriptors (de facto SIFT) has been extensively …

The exponential growth of online videos, along with increasing user involvement in video-
related activities, has been observed as a constant phenomenon during the last decade …

This paper proposes a deep hashing framework, namely, unsupervised deep video hashing
(UDVH), for large-scale video similarity search with the aim to learn compact yet effective …

Near-duplicate video retrieval (NDVR) has recently attracted much research attention due to
the exponential growth of online videos. It has many applications, such as copyright …

When a bug manifests in a user-facing application, it is likely to be exposed through the
graphical user interface (GUI). Given the importance of visual information to the process of …

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 …

Video copy localization aims to precisely localize all the copied segments within a pair of
untrimmed videos in video retrieval applications. Previous methods typically start from frame …

In this work, we propose a deep video hashing (DVH) method for scalable video search.
Unlike most existing video hashing methods that first extract features for each single frame …

This paper shows that an image can be approximately reconstructed based on the output of
a blackbox local description software such as those classically used for image indexing. Our …

Scale-invariant feature transform (SIFT) feature has been widely accepted as an effective
local keypoint descriptor for its invariance to rotation, scale, and lighting changes in images …