Does organisation by similarity assist image browsing?
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Image Categorization by Learning and Reasoning with Regions
The Journal of Machine Learning Research
Locality preserving clustering for image database
Proceedings of the 12th annual ACM international conference on Multimedia
Hierarchical clustering of WWW image search results using visual, textual and link information
Proceedings of the 12th annual ACM international conference on Multimedia
A Sparse Support Vector Machine Approach to Region-Based Image Categorization
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
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
Web image clustering by consistent utilization of visual features and surrounding texts
Proceedings of the 13th annual ACM international conference on Multimedia
Iteratively clustering web images based on link and attribute reinforcements
Proceedings of the 13th annual ACM international conference on Multimedia
IGroup: web image search results clustering
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Kernel Codebooks for Scene Categorization
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part III
Semantic clustering for region-based image retrieval
Journal of Visual Communication and Image Representation
Object Detection with Discriminatively Trained Part-Based Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
Understanding web images by object relation network
Proceedings of the 21st international conference on World Wide Web
CLUE: cluster-based retrieval of images by unsupervised learning
IEEE Transactions on Image Processing
A bag-of-semantics model for image clustering
The Visual Computer: International Journal of Computer Graphics
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This paper presents a novel method to organize a collection of images into a hierarchy of clusters based on image semantics. Given a group of raw images with no metadata as input, our method describes the semantics of each image with a bag-of-semantics model (i.e., a set of meaningful descriptors), which is derived from the image's Object Relation Network [5] - an expressive graph model representing rich semantics for image objects and their relations. We adopt the class hierarchies in a guide ontology as different levels of lenses to view the bag-of-semantics models. Image clusters are automatically extracted by grouping images with the same bag-of-semantics viewed through a certain lens. With a series of coarse-to-fine lenses, images are clustered in a top-down hierarchical manner. In addition, given that users can have different perspectives regarding how images should be clustered, our method allows each user to control the clustering process while browsing, and thus dynamically adjusts the clustering result according to the user's preferences.