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
"GrabCut": interactive foreground extraction using iterated graph cuts
ACM SIGGRAPH 2004 Papers
Cosegmentation of Image Pairs by Histogram Matching - Incorporating a Global Constraint into MRFs
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Scalable Recognition with a Vocabulary Tree
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Pagerank for product image search
Proceedings of the 17th international conference on World Wide Web
A local bag-of-features model for large-scale object retrieval
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part VI
ECCV'10 Proceedings of the 11th European conference on computer vision conference on Computer vision: Part III
Descriptor learning for efficient retrieval
ECCV'10 Proceedings of the 11th European conference on computer vision conference on Computer vision: Part III
The stanford mobile visual search data set
MMSys '11 Proceedings of the second annual ACM conference on Multimedia systems
Mobile product search with bag of hash bits
MM '11 Proceedings of the 19th ACM international conference on Multimedia
A boundary-fragment-model for object detection
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
Object segmentation by alignment of poselet activations to image contours
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Total recall II: Query expansion revisited
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
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Mobile product image search aims at identifying a product, or retrieving similar products from a database based on a photo captured from a mobile phone camera. Application of traditional image retrieval methods (e.g. bag-of-words) to mobile visual search has been shown to be effective in identifying duplicate/near-duplicate photos, near-planar and textured objects such as landmarks, books/cd covers. However, retrieving more general product categories is still a challenging research problem due to variations in viewpoint, illumination, scale, the existence of blur and background clutter in the query image, etc. In this paper, we propose a new approach that can simultaneously extract the product instance from the query, identify the instance, and retrieve visually similar product images. Based on the observation that good query segmentation helps improve retrieval accuracy and good search results provide good priors for segmentation, we formulate our approach in an iterative scheme to improve both query segmentation and retrieval accuracy. To this end, a weighted object mask voting algorithm is proposed based on a spatially-constrained model, which allows robust localization and segmentation of the query object, and achieves significantly better retrieval accuracy than previous methods. We show the effectiveness of our approach by applying it to a large, real-world product image dataset and a new object category dataset.