Unsupervised Learning of Finite Mixture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Blobworld: Image Segmentation Using Expectation-Maximization and Its Application to Image Querying
IEEE Transactions on Pattern Analysis and Machine Intelligence
A continuous probabilistic framework for image matching
Computer Vision and Image Understanding
The Journal of Machine Learning Research
Learning to cluster web search results
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in 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
GCap: Graph-based Automatic Image Captioning
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 9 - Volume 09
User performance versus precision measures for simple search tasks
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
IGroup: web image search results clustering
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Real-time computerized annotation of pictures
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
IGroup: presenting web image search results in semantic clusters
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Annotating Images by Mining Image Search Results
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hierarchical clustering-based navigation of image search results
MM '08 Proceedings of the 16th ACM international conference on Multimedia
Unsupervised image-set clustering using an information theoretic framework
IEEE Transactions on Image Processing
CBSA: content-based soft annotation for multimodal image retrieval using Bayes point machines
IEEE Transactions on Circuits and Systems for Video Technology
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Image on web has become one of the most important information for browsers; however, the large number of results retrieved from images search engine increases the difficulty in finding the intended images. Image search result clustering (ISRC) is a solution to this problem. Currently, the ISRC-based methods separately utilized textual and visual features to present clustering result. In this paper, we proposed a new ISRC method as called Incremental-Annotations-based image search with clustering (IAISC), which adopted annotation as textual features and category model as visual features. IAISC can provide clustering result based on the semantic meaning and visual trail; further, presented by the iteratively structure, a user can obtain the intended image easily. The experimental result shows our method has high precision that the average precision rate is 73.4%; particularly, the precision rate is 96.5% when the user drills down the intended images till the last round. Regarding efficiency, our system is one and a half times as efficient as the previous studies.