Random projection in dimensionality reduction: applications to image and text data
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope
International Journal of Computer Vision
Database-friendly random projections: Johnson-Lindenstrauss with binary coins
Journal of Computer and System Sciences - Special issu on PODS 2001
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
Scale & Affine Invariant Interest Point Detectors
International Journal of Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
A Comparison of Affine Region Detectors
International Journal of Computer Vision
Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Representing shape with a spatial pyramid kernel
Proceedings of the 6th ACM international conference on Image and video retrieval
LIBLINEAR: A Library for Large Linear Classification
The Journal of Machine Learning Research
80 Million Tiny Images: A Large Data Set for Nonparametric Object and Scene Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Evaluation of GIST descriptors for web-scale image search
Proceedings of the ACM International Conference on Image and Video Retrieval
Improving the fisher kernel for large-scale image classification
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
What does classifying more than 10,000 image categories tell us?
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
SURF: speeded up robust features
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Compressed Histogram of Gradients: A Low-Bitrate Descriptor
International Journal of Computer Vision
Accelerating SURF detector on mobile devices
Proceedings of the 20th ACM international conference on Multimedia
IMShare: instantly sharing your mobile landmark images by search-based reconstruction
Proceedings of the 20th ACM international conference on Multimedia
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The rapid development of technologies in both hardware and software have made content-based multimedia services feasible on mobile devices such as smartphones and tablets; and the strong needs for mobile visual search and recognition have been emerging. While many real applications of visual recognition require a large scale recognition systems, the same technologies that support server-based scalable visual recognition may not be feasible on mobile devices due to the resource constraints. Although the client-server framework ensures the scalability, the real-time response subjects to the limitation on network bandwidth. Therefore, the main challenge for mobile visual recognition system should be the recognition bitrate, which is the amount of data transmission under the same recognition performance. For this work, we exploit and compare various strategies such as compact features, feature compression, feature signatures by hashing, image scaling, etc., to enable low bitrate mobile visual recognition. We argue that thumbnail image is a competitive candidate for low bitrate visual recognition because it carries multiple features at once and multi-feature fusion is important as the size of semantic space increases. Our evaluations on two subsets of ImageNet, both contain more than 10,000 images with 19 and 137 categories, verify the efficacy of thumbnail images. We further suggest a new strategy that combines single (local) feature signature and the thumbnail image, which achieves significant bitrate reduction from (average) 102,570 to 4,661 bytes with merely (overall) 10% performance degradation.