Self-organizing maps
Machine Learning - Special issue on applications of machine learning and the knowledge discovery process
Qualitative evaluation of automatic assignment of keywords to images
Information Processing and Management: an International Journal - Special issue: Formal methods for information retrieval
CLAIRE: A modular support vector image indexing and classification system
ACM Transactions on Information Systems (TOIS)
Semantic analysis of real-world images using support vector machine
Expert Systems with Applications: An International Journal
Qualitative evaluation of automatic assignment of keywords to images
Information Processing and Management: an International Journal - Special issue: Formal methods for information retrieval
Content-based image retrieval using a combination of visual features and eye tracking data
Proceedings of the 2010 Symposium on Eye-Tracking Research & Applications
Hybrid neural networks as prediction models
ICAISC'10 Proceedings of the 10th international conference on Artifical intelligence and soft computing: Part II
Identifying a critical threat to privacy through automatic image classification
Proceedings of the first ACM conference on Data and application security and privacy
MLDM'11 Proceedings of the 7th international conference on Machine learning and data mining in pattern recognition
An improved method of breast MRI segmentation with simplified K-means clustered images
Proceedings of the 2011 ACM Symposium on Research in Applied Computation
HAIS'12 Proceedings of the 7th international conference on Hybrid Artificial Intelligent Systems - Volume Part II
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Use of semantic content is one of the major issues which needs to be addressed for improving image retrieval effectiveness. We present a new approach to classify images based on the combination of image processing techniques and hybrid neural networks. Multiple keywords are assigned to an image to represent its main contents, i.e. semantic content. Images are divided into a number of regions and colour and texture features are extracted. The first classifier, a self-organising map (SOM) clusters similar images based on the extracted features. Then, regions of the representative images of these clusters were labeled and used to train the second classifier, composed of several support vector machines (SVMs). Initial experiments on the accuracy of keyword assignment for a small vocabulary are reported.