Paper Info
Title
Cloud-Based Actor Identification With Batch-Orthogonal Local-Sensitive Hashing and Sparse Representation.
Abstract
Recognizing and retrieving multimedia content with movie/TV series actors, especially querying actor-specific videos in large scale video datasets, has attracted much attention in both the video processing and computer vision research field. However, many existing methods have low efficiency both in training and testing processes and also a less than satisfactory performance. Considering these challenges, in this paper, we propose an efficient cloud-based actor identification approach with batch-orthogonal local-sensitive hashing (BOLSH) and multi-task joint sparse representation classification. Our approach is featured by the following: 1) videos from movie/TV series are segmented into shots with the cloud-based shot boundary detection; 2) while faces in each shot are detected and tracked, the cloud-based BOLSH is then implemented on these faces for feature description; 3) the sparse representation is then adopted for actor identification in each shot; and 4) finally, a simple application, actor-specific shots retrieval is realized to verify our approach. We conduct extensive experiments and empirical evaluations on a large scale dataset, to demonstrate the satisfying performance of our approach considering both accuracy and efficiency.
Year
DOI
Venue
2016
10.1109/TMM.2016.2579305
IEEE Trans. Multimedia
Keywords
Field
DocType
Videos,Cloud computing,Motion pictures,Feature extraction,Face recognition,Sensors,Streaming media
Facial recognition system,Computer vision,Video processing,Computer science,Sparse approximation,Feature extraction,Boundary detection,Artificial intelligence,Hash function,Feature description,Cloud computing
Journal
Volume
Issue
ISSN
18
9
1520-9210
Citations 
PageRank 
References 
3
0.44
36
Authors
5
Name
Order
Citations
PageRank
Guangyu Gao1336.50
Chi Harold Liu2109172.90
Min Chen3112162.51
Song Guo43431278.71
Kin K. Leung52463183.60