Solving the multiple instance problem with axis-parallel rectangles
Artificial Intelligence
An automatic hierarchical image classification scheme
MULTIMEDIA '98 Proceedings of the sixth ACM international conference on Multimedia
Image classification and querying using composite region templates
Computer Vision and Image Understanding - Special issue on content-based access for image and video libraries
Numerical Recipes in C: The Art of Scientific Computing
Numerical Recipes in C: The Art of Scientific Computing
Content-Based Image Retrieval Using Multiple-Instance Learning
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Multiple-Instance Learning for Natural Scene Classification
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Automatic image annotation and retrieval using cross-media relevance models
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Introduction to MPEG-7: Multimedia Content Description Interface
Introduction to MPEG-7: Multimedia Content Description Interface
Comparing Support Vector Machines with Gaussian Kernels to Radial Basis Function Classifiers
Comparing Support Vector Machines with Gaussian Kernels to Radial Basis Function Classifiers
Automatic Linguistic Indexing of Pictures by a Statistical Modeling Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image Categorization by Learning and Reasoning with Regions
The Journal of Machine Learning Research
Support vector machines for histogram-based image classification
IEEE Transactions on Neural Networks
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In this paper, a novel and efficient automatic image categorization system is proposed. This system integrates the MIL-based and global-feature-based SVMs for categorization. The IPs (Instance Prototypes) are derived from the segmented regions by applying MIL on the training images from different categories. The IPs-based image features are further used as inputs to a set of SVMs to find the optimum hyperplanes for categorizing training images. Similarly, global image features, including color histogram and edge histogram, are fed into another set of SVMs. For each test image, two sets of image features are constructed and sent to the two respective sets of SVMs. The decision values from two sets of SVMs are finally incorporated to obtain the final categorization results. The empirical results demonstrate that the proposed system outperforms the peer systems in terms of both efficiency and accuracy.