Scalable Recognition with a Vocabulary Tree
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Rate-efficient, real-time cd cover recognition on a camera-phone
MM '08 Proceedings of the 16th ACM international conference on Multimedia
Inferring semantic concepts from community-contributed images and noisy tags
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Beyond distance measurement: constructing neighborhood similarity for video annotation
IEEE Transactions on Multimedia - Special section on communities and media computing
Location coding for mobile image retrieval
Proceedings of the 5th International ICST Mobile Multimedia Communications Conference
Improving Bag-of-Features for Large Scale Image Search
International Journal of Computer Vision
Proceedings of the international conference on Multimedia
Low latency image retrieval with progressive transmission of CHoG descriptors
Proceedings of the 2010 ACM multimedia workshop on Mobile cloud media computing
Location Discriminative Vocabulary Coding for Mobile Landmark Search
International Journal of Computer Vision
City-scale landmark identification on mobile devices
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Robust Spatial Matching for Object Retrieval and Its Parallel Implementation on GPU
IEEE Transactions on Multimedia
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Progressive transmission is very effective to reduce retrieval latency in mobile visual search. However, the acceleration effects of existing progressive transmission strategies are often limited because of the neglect of geometric information in the query image. This paper proposes an effective and efficient geometric context-preserving progressive transmission method, which is suitable for mobile visual search. Here a query image is divided into blocks and local features in the same block are used as query units rather than a single feature. Since clustered features with geometric information are more discriminative, only a few of them could support correct matching with high precision. Thus our method significantly decreases the number of features needed for transmission, and dramatically reduces the retrieval latency. Experiments on Stanford dataset for mobile visual search show that, with comparable precision, we uses 43% less retrieval time than existing progressive transmission method. Moreover, we establish and release a large-scale image dataset called MVSBench which is more difficult and suitable for mobile visual search. It contains 75500 images and considers many variations like view change, blur, scale, illumination and rotation. MVSBench is another major contribution of this paper, and our method also outperforms other strategies on this dataset.