A Decision Tree Approach for Scene Pattern Recognition and Extraction in Snooker Videos
ICIAR '09 Proceedings of the 6th International Conference on Image Analysis and Recognition
Deep exploration for experiential image retrieval
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Using visual context and region semantics for high-level concept detection
IEEE Transactions on Multimedia - Special issue on integration of context and content
An interactive approach for filtering out junk images from keyword-based google search results
IEEE Transactions on Circuits and Systems for Video Technology
Hidden-concept driven image decomposition towards semi-supervised multi-label image annotation
Proceedings of the First International Conference on Internet Multimedia Computing and Service
Performance measures for multilabel evaluation: a case study in the area of image classification
Proceedings of the international conference on Multimedia information retrieval
Multimedia data mining: state of the art and challenges
Multimedia Tools and Applications
Semantic context inference in multimedia search
The future internet
Semantic hierarchies for image annotation: A survey
Pattern Recognition
Multifeature analysis and semantic context learning for image classification
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Improving bathymetric images exploration: A data mining approach
Computers & Geosciences
Learning group-based dictionaries for discriminative image representation
Pattern Recognition
Image categorization using a semantic hierarchy model with sparse set of salient regions
Frontiers of Computer Science: Selected Publications from Chinese Universities
A semantic image classifier based on hierarchical fuzzy association rule mining
Multimedia Tools and Applications
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In this paper, we have proposed a novel framework for mining multilevel image semantics via hierarchical classification. To bridge the semantic gap more successfully, salient objects are used to characterize the intermediate image semantics effectively. The salient objects are defined as the connected image regions that capture the dominant visual properties linked to the corresponding physical objects in an image. To achieve a more reliable and tractable concept learning in high-dimensional feature space, a novel algorithm called product of mixture-experts (PoM) is proposed to reduce the size of training images and speed up concept learning. A novel hierarchical concept learning algorithm is proposed by incorporating concept ontology and multitask learning to enhance the discrimination power of the concept models and reduce the computational complexity for learning the concept models for large amount of image concepts, which may have huge intra-concept variations and inter-concept similarities on their visual properties. A hyperbolic image visualization algorithm has been developed for allowing users to specify their queries easily and assess the query results interactively. Our experiments on large-scale image collections have also obtained very positive results.