Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope
International Journal of Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
A Bayesian Hierarchical Model for Learning Natural Scene Categories
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
A Comparison of Affine Region Detectors
International Journal of Computer Vision
Extracting Subimages of an Unknown Category from a Set of Images
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
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
Scene Classification Using a Hybrid Generative/Discriminative Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image retrieval: Ideas, influences, and trends of the new age
ACM Computing Surveys (CSUR)
Annotating Images by Mining Image Search Results
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hamming Embedding and Weak Geometric Consistency for Large Scale Image Search
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Automatic video tagging using content redundancy
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Inferring semantic concepts from community-contributed images and noisy tags
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Unified video annotation via multigraph learning
IEEE Transactions on Circuits and Systems for Video Technology
NUS-WIDE: a real-world web image database from National University of Singapore
Proceedings of the ACM International Conference on Image and Video Retrieval
Mining multi-tag association for image tagging
World Wide Web
Packed Dense Interest Points for Scene Image Retrieval
ICIG '11 Proceedings of the 2011 Sixth International Conference on Image and Graphics
Combining attributes and Fisher vectors for efficient image retrieval
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
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In this paper, we propose a novel method for scene image retrieval in which the semantic meaning of an image and a new low-level feature are combined. The fluid nature of scene images makes learning semantics essential in our retrieval task. Compared to a general image, a scene image contains large regions of low contrast, which makes it difficult for a method to extract features that has good coverage of the entire image and assurance of relatively high repeatability. Given a scene image as a query, a collection of images is first retrieved by some search engines based on the images' semantic meanings. The candidate images are re-ranked by adapting an asymmetric piece-to-image matching scheme based on their visual similarities with the query image, using its visual signature consists of some packed dense interest points. Our method is evaluated on an Outdoor Scene Recognition (OSR) dataset and an NUS-WIDE dataset. It has demonstrated the improvements of our method over other conventional approaches.