A Validity Measure for Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
WordNet: a lexical database for English
Communications of the ACM
Color image processing and applications
Color image processing and applications
Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
Comparing discriminating transformations and SVM for learning during multimedia retrieval
MULTIMEDIA '01 Proceedings of the ninth ACM international conference on Multimedia
Blobworld: Image Segmentation Using Expectation-Maximization and Its Application to Image Querying
IEEE Transactions on Pattern Analysis and Machine Intelligence
Visually Searching the Web for Content
IEEE MultiMedia
Multiple-Instance Learning for Natural Scene Classification
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Combining Textual and Visual Cues for Content-Based Image Retrieval on the World Wide Web
CBAIVL '98 Proceedings of the IEEE Workshop on Content - Based Access of Image and Video Libraries
Bayesian Relevance Feedback for Content-Based Image Retrieval
CBAIVL '00 Proceedings of the IEEE Workshop on Content-based Access of Image and Video Libraries (CBAIVL'00)
Automatic Identification of Perceptually Important Regions in an Image
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 1 - Volume 1
WebSeer: An Image Search Engine for the World Wide Web
WebSeer: An Image Search Engine for the World Wide Web
Automatic Linguistic Indexing of Pictures by a Statistical Modeling Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Semantic-meaningful content-based image retrieval in wavelet domain
MIR '03 Proceedings of the 5th ACM SIGMM international workshop on Multimedia information retrieval
Content-based image retrieval by clustering
MIR '03 Proceedings of the 5th ACM SIGMM international workshop on Multimedia information retrieval
Hierarchical clustering of WWW image search results using visual, textual and link information
Proceedings of the 12th annual ACM international conference on Multimedia
Learning Object Categories from Google"s Image Search
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Probabilistic web image gathering
Proceedings of the 7th ACM SIGMM international workshop on Multimedia information retrieval
Content-based image retrieval: approaches and trends of the new age
Proceedings of the 7th ACM SIGMM international workshop on Multimedia information retrieval
Clustering presentation of web image retrieval results using textual information and image features
IMSA'06 Proceedings of the 24th IASTED international conference on Internet and multimedia systems and applications
A histogram-based approach for object-based query-by-shape-and-color in image and video databases
Image and Vision Computing
Relevance feedback: a power tool for interactive content-based image retrieval
IEEE Transactions on Circuits and Systems for Video Technology
A novel region-based approach to visual concept modeling using web images
MM '08 Proceedings of the 16th ACM international conference on Multimedia
Incorporating concept ontology into multi-level image indexing
Proceedings of the First International Conference on Internet Multimedia Computing and Service
Region-based automatic web image selection
Proceedings of the international conference on Multimedia information retrieval
Web image gathering with a part-based object recognition method
MMM'08 Proceedings of the 14th international conference on Advances in multimedia modeling
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In this paper, a novel approach for discovering visual patterns associated with semantic concepts using web image resources is proposed.This approach can be used to improve the performance in image clustering and retrieval, image annotation, and other applications such as object recognition. Exploring the rich information in web images that represent semantic concepts as both visual content and text information, this research attempts to effectively learn intrinsic patterns related to semantic concepts. Because the quality of learning algorithms is strongly related to the selection of positive and negative samples, positive samples are first selected effectively, then negative samples are determined reliably based on the selected positive samples. Finally, a good quality visual model associated with a semantic concept is built through an unsupervised learning process. The proposed scheme is completely automatic,needing no human intervention,and is robust and reliable for generic images. Experimental results demonstrate the effectiveness of the proposed approach.