C4.5: programs for machine learning
C4.5: programs for machine learning
Performance evaluation in content-based image retrieval: overview and proposals
Pattern Recognition Letters - Special issue on image/video indexing and retrieval
Using One-Class and Two-Class SVMs for Multiclass Image Annotation
IEEE Transactions on Knowledge and Data Engineering
Automatic image annotation and retrieval using weighted feature selection
Multimedia Tools and Applications
A survey of content-based image retrieval with high-level semantics
Pattern Recognition
Expert Systems with Applications: An International Journal
Patterns of semantic relations to improve image content search
Web Semantics: Science, Services and Agents on the World Wide Web
Image retrieval: Ideas, influences, and trends of the new age
ACM Computing Surveys (CSUR)
A survey of methods for image annotation
Journal of Visual Languages and Computing
Constructing a decision tree from data with hierarchical class labels
Expert Systems with Applications: An International Journal
Semantic clustering for region-based image retrieval
Journal of Visual Communication and Image Representation
The Top Ten Algorithms in Data Mining
The Top Ten Algorithms in Data Mining
The segmented and annotated IAPR TC-12 benchmark
Computer Vision and Image Understanding
Fusing semantic aspects for image annotation and retrieval
Journal of Visual Communication and Image Representation
Semantic hierarchies for image annotation: A survey
Pattern Recognition
A review on automatic image annotation techniques
Pattern Recognition
Top-down induction of decision trees classifiers - a survey
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
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This paper proposes a semantic-based image retrieval approach which refers to the ability of using keywords for searching within image datasets. This is possible by adding some textual metadata, called image annotation. Combination of classification and regression in decision tree (DT) has been employed for multi-labeling image annotation in which, more than one label will be considered for every single tuple. In the proposed approach, all concepts and their corresponding ranks will be stored in each DT leaf node instead of storing only a concept or a rank. We have used a hierarchical network of semantics to achieve a better performance. The main idea behind our approach is that in each leaf node, the system should give a higher rank to concepts with highest degree of purity and details according to prepared hierarchical semantic network. A segmented, feature extracted and annotated image dataset, SAIAPR-TC12, has been used for evaluation. A hierarchy of 256 semantic concepts which have been used in annotation process, made it very suitable for testing the approach. Experimental results confirmed that our approach illustrates better performance in comparison with single-labeling approaches which only assign one class to every single tuple and only support linear relationship among concepts.