Term-weighting approaches in automatic text retrieval
Information Processing and Management: an International Journal
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
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
A Comparison of Affine Region Detectors
International Journal of Computer Vision
Scalable Recognition with a Vocabulary Tree
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
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
Local Word Bag Model for Text Categorization
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
HOTPAPER demonstration: multimedia interaction with paper using mobile phones
MM '08 Proceedings of the 16th ACM international conference on Multimedia
Proceedings of the international conference on Multimedia
The stanford mobile visual search data set
MMSys '11 Proceedings of the second annual ACM conference on Multimedia systems
Image retrieval with geometry-preserving visual phrases
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Search by mobile image based on visual and spatial consistency
ICME '11 Proceedings of the 2011 IEEE International Conference on Multimedia and Expo
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More powerful mobile devices stimulate mobile visual search to become a popular and unique image retrieval application. A number of challenges come up with such application, resulting from appearance variations in mobile images. Performance of state-of-the-art image retrieval systems is improved using bag-of-words approaches. However, for visual search by mobile images with large variations, there are at least two critical issues unsolved: (1) the loss of features discriminative power due to quantization; and (2) the underuse of spatial relationships among visual words. To address both issues, this paper presents a novel visual search method based on feature grouping and local soft match, which considers properties of mobile images and couples visual and spatial information consistently. First features of the query image are grouped using both matched visual features and their spatial relationships; and then grouped features are softly matched to alleviate quantization loss. An efficient score scheme is devised to utilize inverted file index and compared with vocabulary-guided pyramid kernels. Finally experiments on Stanford mobile visual search database and a collected database with more than one million images show that the proposed method achieves promising improvement over the approach with a vocabulary tree, especially when large variations exist in query images.