Name
Abstract | ||
|---|---|---|
Retrieving faces from large mess of videos is an attractive research topic with wide range of applications. Its challenging problems are large intra-class variations, and tremendous time and space complexity. In this paper, we develop a new deep convolutional neural network (deep CNN) to learn discriminative and compact binary representations of faces for face video retrieval. The network integrates feature extraction and hash learning into a unified optimization framework for the optimal compatibility of feature extractor and hash functions. In order to better initialize the network, the low-rank discriminative binary hashing is proposed to pre-learn hash functions during the training procedure. Our method achieves excellent performances on two challenging TV-Series datasets. |
Year | Venue | Field |
|---|---|---|
2016 | THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE | Pattern recognition,Video retrieval,Computer science,Convolutional neural network,Feature hashing,Feature extraction,Hash function,Artificial intelligence,Deep learning,Discriminative model,Machine learning,Binary number |
DocType | Citations | PageRank |
Conference | 8 | 0.49 |
References | Authors | |
16 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhen Dong | 1 | 58 | 4.78 |
| Su Jia | 2 | 13 | 1.23 |
| Tianfu Wu | 3 | 331 | 26.72 |
| Mingtao Pei | 4 | 246 | 26.35 |