Video Google: A Text Retrieval Approach to Object Matching in Videos
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Early versus late fusion in semantic video analysis
Proceedings of the 13th annual ACM international conference on Multimedia
Early versus late fusion in semantic video analysis
Proceedings of the 13th annual ACM international conference on Multimedia
Multi-probe LSH: efficient indexing for high-dimensional similarity search
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Speeded-Up Robust Features (SURF)
Computer Vision and Image Understanding
Effective and Efficient Query Processing for Video Subsequence Identification
IEEE Transactions on Knowledge and Data Engineering
Real-time near-duplicate elimination for web video search with content and context
IEEE Transactions on Multimedia - Special issue on integration of context and content
Mobile interaction techniques for interrelated videos
CHI '10 Extended Abstracts on Human Factors in Computing Systems
Coherent bag-of audio words model for efficient large-scale video copy detection
Proceedings of the ACM International Conference on Image and Video Retrieval
Scalable clip-based near-duplicate video detection with ordinal measure
Proceedings of the ACM International Conference on Image and Video Retrieval
Real-time large scale near-duplicate web video retrieval
Proceedings of the international conference on Multimedia
Towards low bit rate mobile visual search with multiple-channel coding
MM '11 Proceedings of the 19th ACM international conference on Multimedia
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Fast Matching of Binary Features
CRV '12 Proceedings of the 2012 Ninth Conference on Computer and Robot Vision
Mobile product search with Bag of Hash Bits and boundary reranking
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Fast search in Hamming space with multi-index hashing
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Proceedings of the 20th ACM international conference on Multimedia
Accelerating SURF detector on mobile devices
Proceedings of the 20th ACM international conference on Multimedia
Sketch-based image retrieval on mobile devices using compact hash bits
Proceedings of the 20th ACM international conference on Multimedia
Local visual words coding for low bit rate mobile visual search
Proceedings of the 20th ACM international conference on Multimedia
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Mobile video is quickly becoming a mass consumer phenomenon. More and more people are using their smartphones to search and browse video content while on the move. In this paper, we have developed an innovative instant mobile video search system through which users can discover videos by simply pointing their phones at a screen to capture a very few seconds of what they are watching. The system is able to index large-scale video data using a new layered audio-video indexing approach in the cloud, as well as extract light-weight joint audio-video signatures in real time and perform progressive search on mobile devices. Unlike most existing mobile video search applications that simply send the original video query to the cloud, the proposed mobile system is one of the first attempts at instant and progressive video search leveraging the light-weight computing capacity of mobile devices. The system is characterized by four unique properties: 1) a joint audio-video signature to deal with the large aural and visual variances associated with the query video captured by the mobile phone, 2) layered audio-video indexing to holistically exploit the complementary nature of audio and video signals, 3) light-weight fingerprinting to comply with mobile processing capacity, and 4) a progressive query process to significantly reduce computational costs and improve the user experience---the search process can stop anytime once a confident result is achieved. We have collected 1,400 query videos captured by 25 mobile users from a dataset of 600 hours of video. The experiments show that our system outperforms state-of-the-art methods by achieving 90.79% precision when the query video is less than 10 seconds and 70.07% even when the query video is less than 5 seconds.