WordNet: a lexical database for English
Communications of the ACM
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Discovering Objects and their Localization in Images
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Image annotations by combining multiple evidence & wordNet
Proceedings of the 13th annual ACM international conference on Multimedia
PARAgrab: a comprehensive architecture for web image management and multimodal querying
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Semantic taxonomy induction from heterogenous evidence
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Hierarchical classification for automatic image annotation
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Ontology-enriched semantic space for video search
Proceedings of the 15th international conference on Multimedia
Toward Bridging the Annotation-Retrieval Gap in Image Search
IEEE MultiMedia
Randomized Clustering Forests for Image Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
80 Million Tiny Images: A Large Data Set for Nonparametric Object and Scene Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Constructing Category Hierarchies for Visual Recognition
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part IV
International Journal of Computer Vision
2009 Special Issue: Cortical circuits for perceptual inference
Neural Networks
Scale-invariant visual language modeling for object categorization
IEEE Transactions on Multimedia - Special issue on integration of context and content
The Pascal Visual Object Classes (VOC) Challenge
International Journal of Computer Vision
Semantics-preserving bag-of-words models and applications
IEEE Transactions on Image Processing
What does classifying more than 10,000 image categories tell us?
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
Semantic hierarchies for image annotation: A survey
Pattern Recognition
A Saliency Detection Model Based on Multi-feature Fusion
CIS '11 Proceedings of the 2011 Seventh International Conference on Computational Intelligence and Security
Building semantic hierarchies faithful to image semantics
MMM'12 Proceedings of the 18th international conference on Advances in Multimedia Modeling
Flickr Distance: A Relationship Measure for Visual Concepts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mining Multilevel Image Semantics via Hierarchical Classification
IEEE Transactions on Multimedia
IEEE Transactions on Image Processing
Context-Aware Saliency Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hierarchical image annotation using semantic hierarchies
Proceedings of the 21st ACM international conference on Information and knowledge management
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Image categorization in massive image database is an important problem. This paper proposes an approach for image categorization, using sparse set of salient semantic information and hierarchy semantic label tree (HSLT) model. First, to provide more critical image semantics, the proposed sparse set of salient regions only at the focuses of visual attention instead of the entire scene was formed by our proposed saliency detection model with incorporating low and high level feature and Shotton's semantic texton forests (STFs) method. Second, we also propose a new HSLT model in terms of the sparse regional semantic information to automatically build a semantic image hierarchy, which explicitly encodes a general to specific image relationship. And last, we archived image dataset using image hierarchical semantic, which is help to improve the performance of image organizing and browsing. Extension experimental results showed that the use of semantic hierarchies as a hierarchical organizing framework provides a better image annotation and organization, improves the accuracy and reduces human's effort.