Supervised Recurrent Hashing for Large Scale Video Retrieval
Authors:
Open Access
- 1 October 2016
- conference paper
- Published by Association for Computing Machinery (ACM)
- p. 272-276
Abstract
Hashing for large-scale multimedia is a popular research topic, attracting much attention in computer vision and visual information retrieval. Previous works mostly focus on hashing the images and texts while the approaches designed for videos are limited. In this paper, we propose a \textit{Supervised Recurrent Hashing} (SRH) that explores the discriminative representation obtained by deep neural networks to design hashing approaches. The long-short term memory (LSTM) network is deployed to model the structure of video samples. The max-pooling mechanism is introduced to embedding the frames into fixed-length representations that are fed into supervised hashing loss. Experiments on UCF-101 dataset demonstrate that the proposed method can significantly outperforms several state-of-the-art methods.Keywords
Funding Information
- 973 Plan, China (2015CB856004)
- National Natural Science Foundation of China (61572315)
This publication has 12 references indexed in Scilit:
- Cross-modality hashing with partial correspondence2015
- Long-term recurrent convolutional networks for visual recognition and description2015
- Caffe2014
- CNN Features Off-the-Shelf: An Astounding Baseline for Recognition2014
- Large-Scale Video Hashing via Structure Learning2013
- Submodular video hashing2012
- Semi-Supervised Hashing for Large-Scale SearchIEEE Transactions on Pattern Analysis and Machine Intelligence, 2012
- Multiple feature hashing for real-time large scale near-duplicate video retrieval2011
- Locality-sensitive hashing scheme based on p-stable distributions2004
- Long Short-Term MemoryNeural Computation, 1997