Journal ArticleDOI
Effective Multiple Feature Hashing for Large-Scale Near-Duplicate Video Retrieval
Reads0
Chats0
TLDR
A novel approach-Multiple Feature Hashing (MFH) to tackle both the accuracy and the scalability issues of NDVR and shows that the proposed method outperforms the state-of-the-art techniques in both accuracy and efficiency.Abstract:
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 protection, automatic video tagging and online video monitoring. Many existing approaches use only a single feature to represent a video for NDVR. However, a single feature is often insufficient to characterize the video content. Moreover, while the accuracy is the main concern in previous literatures, the scalability of NDVR algorithms for large scale video datasets has been rarely addressed. In this paper, we present a novel approach-Multiple Feature Hashing (MFH) to tackle both the accuracy and the scalability issues of NDVR. MFH preserves the local structural information of each individual feature and also globally considers the local structures for all the features to learn a group of hash functions to map the video keyframes into the Hamming space and generate a series of binary codes to represent the video dataset. We evaluate our approach on a public video dataset and a large scale video dataset consisting of 132,647 videos collected from YouTube by ourselves. This dataset has been released (http://itee.uq.edu.au/shenht/UQ_VIDEO/). The experimental results show that the proposed method outperforms the state-of-the-art techniques in both accuracy and efficiency.read more
Citations
More filters
Journal ArticleDOI
A Survey on Learning to Hash
TL;DR: In this paper, a comprehensive survey of the learning to hash algorithms is presented, categorizing them according to the manners of preserving the similarities into: pairwise similarity preserving, multi-wise similarity preservation, implicit similarity preserving and quantization, and discuss their relations.
Journal ArticleDOI
Video Captioning With Attention-Based LSTM and Semantic Consistency
TL;DR: A novel end-to-end framework named aLSTMs, an attention-based LSTM model with semantic consistency, to transfer videos to natural sentences with competitive or even better results than the state-of-the-art baselines for video captioning in both BLEU and METEOR.
Posted Content
Hashing for Similarity Search: A Survey
TL;DR: This paper presents a survey on one of the main solutions to approximate search, hashing, which has been widely studied since the pioneering work locality sensitive hashing, and divides the hashing algorithms into two main categories.
Journal ArticleDOI
PROVID: Progressive and Multimodal Vehicle Reidentification for Large-Scale Urban Surveillance
TL;DR: This paper proposes PROVID, a PROgressive Vehicle re-IDentification framework based on deep neural networks, which not only utilizes the multimodality data in large-scale video surveillance, such as visual features, license plates, camera locations, and contextual information, but also considers vehicle reidentification in two progressive procedures: coarse- to-fine search in the feature domain, and near-to-distantsearch in the physical space.
Journal ArticleDOI
Deep Multi-View Enhancement Hashing for Image Retrieval
TL;DR: Zhang et al. as discussed by the authors proposed a supervised multi-view hash model which can enhance the multiview information through neural networks, and the proposed method utilizes an effective view stability evaluation method to actively explore the relationship among views, which will affect the optimization direction of the entire network.
References
More filters
Book ChapterDOI
SURF: speeded up robust features
TL;DR: A novel scale- and rotation-invariant interest point detector and descriptor, coined SURF (Speeded Up Robust Features), which approximates or even outperforms previously proposed schemes with respect to repeatability, distinctiveness, and robustness, yet can be computed and compared much faster.
Journal ArticleDOI
A performance evaluation of local descriptors
TL;DR: It is observed that the ranking of the descriptors is mostly independent of the interest region detector and that the SIFT-based descriptors perform best and Moments and steerable filters show the best performance among the low dimensional descriptors.
Proceedings ArticleDOI
A performance evaluation of local descriptors
TL;DR: It is observed that the ranking of the descriptors is mostly independent of the interest region detector and that the SIFT-based descriptors perform best and Moments and steerable filters show the best performance among the low dimensional descriptors.
Proceedings ArticleDOI
Locality-sensitive hashing scheme based on p-stable distributions
TL;DR: A novel Locality-Sensitive Hashing scheme for the Approximate Nearest Neighbor Problem under lp norm, based on p-stable distributions that improves the running time of the earlier algorithm and yields the first known provably efficient approximate NN algorithm for the case p<1.
Proceedings Article
Spectral Hashing
TL;DR: The problem of finding a best code for a given dataset is closely related to the problem of graph partitioning and can be shown to be NP hard and a spectral method is obtained whose solutions are simply a subset of thresholded eigenvectors of the graph Laplacian.