Texture Features for Browsing and Retrieval of Image Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computer Vision
Image Indexing Using Color Correlograms
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
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
An empirical study of smoothing techniques for language modeling
ACL '96 Proceedings of the 34th annual meeting on Association for Computational Linguistics
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
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
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
MM '08 Proceedings of the 16th ACM international conference on Multimedia
Understanding tag-cloud and visual features for better annotation of concepts in NUS-WIDE dataset
WSMC '09 Proceedings of the 1st workshop on Web-scale multimedia corpus
NUS-WIDE: a real-world web image database from National University of Singapore
Proceedings of the ACM International Conference on Image and Video Retrieval
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This paper focuses on improving the semi-manual method for web image concept annotation. By sufficiently studying the characteristics of tag and visual feature, we propose the Grouping-Based-Precision & Recall-Aided (GBPRA) feature selection strategy for concept annotation. Specifically, for visual features, we construct a more robust middle level feature by concatenating the k-NN results for each type of visual feature. For tag, we construct a concept-tag co-occurrence matrix, based on which the probability of an image belonging to certain concept can be calculated. By understanding the tags' quality and groupings' semantic depth, we propose a grouping based feature selection method; by studying the tags' distribution, we adopt Precision and Recall as a complementary indicator for feature selection. In this way, the advantages of both tags and visual features are boosted. Experimental results show our method can achieve very high Average Precision, which greatly facilitates the annotation of large-scale web image dataset.