Improving similarity measures of histograms using smoothing projections
Pattern Recognition Letters
Consistency-based search in feature selection
Artificial Intelligence
Hybrid Genetic Algorithms for Feature Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient Feature Selection via Analysis of Relevance and Redundancy
The Journal of Machine Learning Research
Boosting the distance estimation
Pattern Recognition Letters
Automatic image annotation and retrieval using weighted feature selection
Multimedia Tools and Applications
A Wrapper for Feature Selection Based on Mutual Information
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 02
Incorporating multiple SVMs for automatic image annotation
Pattern Recognition
A survey of content-based image retrieval with high-level semantics
Pattern Recognition
Visual learning of texture descriptors for facial expression recognition in thermal imagery
Computer Vision and Image Understanding
Supervised Learning of Semantic Classes for Image Annotation and Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Toward Bridging the Annotation-Retrieval Gap in Image Search
IEEE MultiMedia
Feature selection for automatic image annotation
DAGM'06 Proceedings of the 28th conference on Pattern Recognition
Similarity-based online feature selection in content-based image retrieval
IEEE Transactions on Image Processing
The MPEG-7 visual standard for content description-an overview
IEEE Transactions on Circuits and Systems for Video Technology
IEEE Transactions on Circuits and Systems for Video Technology
MPEG-7 visual shape descriptors
IEEE Transactions on Circuits and Systems for Video Technology
Semantic Image Segmentation and Object Labeling
IEEE Transactions on Circuits and Systems for Video Technology
An Object- and User-Driven System for Semantic-Based Image Annotation and Retrieval
IEEE Transactions on Circuits and Systems for Video Technology
A novel image retrieval model based on the most relevant features
Knowledge-Based Systems
Feature subset selection using differential evolution and a statistical repair mechanism
Expert Systems with Applications: An International Journal
Rule induction based-on coevolutionary algorithms for image annotation
ACIIDS'11 Proceedings of the Third international conference on Intelligent information and database systems - Volume Part II
Image feature selection based on ant colony optimization
AI'11 Proceedings of the 24th international conference on Advances in Artificial Intelligence
Improving the ranking quality of medical image retrieval using a genetic feature selection method
Decision Support Systems
The effectiveness of image features based on fractal image coding for image annotation
Expert Systems with Applications: An International Journal
Efficient ant colony optimization for image feature selection
Signal Processing
Computers and Electronics in Agriculture
Human action recognition optimization based on evolutionary feature subset selection
Proceedings of the 15th annual conference on Genetic and evolutionary computation
Research on clinical decision support systems development for atrophic gastritis screening
Expert Systems with Applications: An International Journal
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Machine learning techniques for feature selection, which include the optimization of feature descriptor weights and the selection of optimal feature descriptor subset, are desirable to enhance the performance of image annotation systems. In our system, the multimedia content description interface (MPEG-7) image feature descriptors consisting of color descriptors, texture descriptors and shape descriptors are employed to represent low-level image features. We use a real coded chromosome genetic algorithm and k-nearest neighbor (k-NN) classification accuracy as fitness function to optimize the weights of MPEG-7 image feature descriptors. A binary one and k-NN classification accuracy combining with the size of feature descriptor subset as fitness function are used to select optimal MPEG-7 feature descriptor subset. Furthermore, a bi-coded chromosome genetic algorithm is used for the simultaneity of weight optimization and descriptor subset selection, whose fitness function is the same as that of the binary one. The experimental results over 2000 classified Corel images show that with the real coded genetic algorithm, the binary coded one and the bi-coded one, the accuracies of image annotation system are improved by 7%, 9% and 13.6%, respectively, comparing to the method without machine learning. Furthermore, 2 of 25 MPEG-7 feature descriptors are selected with the binary coded genetic algorithm and four with the bi-coded one, which may improve the efficiency of system significantly.