A Markov Random Field Model-Based Approach to Image Interpretation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Region Competition: Unifying Snakes, Region Growing, and Bayes/MDL for Multiband Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Convergence Properties of the Nelder--Mead Simplex Method in Low Dimensions
SIAM Journal on Optimization
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Indoor-Outdoor Image Classification
CAIVD '98 Proceedings of the 1998 International Workshop on Content-Based Access of Image and Video Databases (CAIVD '98)
Image Indexing Using Color Correlograms
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
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
The Journal of Machine Learning Research
Image Categorization by Learning and Reasoning with Regions
The Journal of Machine Learning Research
Image classification for content-based indexing
IEEE Transactions on Image Processing
CBSA: content-based soft annotation for multimodal image retrieval using Bayes point machines
IEEE Transactions on Circuits and Systems for Video Technology
Support vector machines for histogram-based image classification
IEEE Transactions on Neural Networks
Multi-objective Genetic Programming for Multiple Instance Learning
ECML '07 Proceedings of the 18th European conference on Machine Learning
Feature selection based-on genetic algorithm for image annotation
Knowledge-Based Systems
Expert Systems with Applications: An International Journal
A fuzzy combined learning approach to content-based image retrieval
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
Multiple instance learning via margin maximization
Applied Numerical Mathematics
Improving the accuracy of global feature fusion based image categorisation
SAMT'07 Proceedings of the semantic and digital media technologies 2nd international conference on Semantic Multimedia
Empirical investigations on benchmark tasks for automatic image annotation
VISUAL'07 Proceedings of the 9th international conference on Advances in visual information systems
Image annotation by incorporating word correlations into multi-class SVM
ICNC'09 Proceedings of the 5th international conference on Natural computation
Proceedings of the ACM International Conference on Image and Video Retrieval
Grammar guided genetic programming for multiple instance learning: an experimental study
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Multiple Instance Learning with Multiple Objective Genetic Programming for Web Mining
Applied Soft Computing
An HMM-SVM-based automatic image annotation approach
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part IV
Content-based histopathology image retrieval using a kernel-based semantic annotation framework
Journal of Biomedical Informatics
A review on automatic image annotation techniques
Pattern Recognition
Multiple instance learning for classifying students in learning management systems
Expert Systems with Applications: An International Journal
Multi-view learning from imperfect tagging
Proceedings of the 20th ACM international conference on Multimedia
HyDR-MI: A hybrid algorithm to reduce dimensionality in multiple instance learning
Information Sciences: an International Journal
Using Hilbert scan on statistical color space partitioning
Computers and Electrical Engineering
Learning with limited and noisy tagging
Proceedings of the 21st ACM international conference on Multimedia
Structural image retrieval using automatic image annotation and region based inverted file
Journal of Visual Communication and Image Representation
Learning discriminative localization from weakly labeled data
Pattern Recognition
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In this paper, a novel automatic image annotation system is proposed, which integrates two sets of support vector machines (SVMs), namely the multiple instance learning (MIL)-based and global-feature-based SVMs, for annotation. The MIL-based bag features are obtained by applying MIL on the image blocks, where the enhanced diversity density (DD) algorithm and a faster searching algorithm are applied to improve the efficiency and accuracy. They are further input to a set of SVMs for finding the optimum hyperplanes to annotate training images. Similarly, global color and texture features, including color histogram and modified edge histogram, are fed into another set of SVMs for categorizing training images. Consequently, two sets of image features are constructed for each test image and are, respectively, sent to the two sets of SVMs, whose outputs are incorporated by an automatic weight estimation method to obtain the final annotation results. Our proposed annotation approach demonstrates a promising performance for an image database of 12000 general-purpose images from COREL, as compared with some current peer systems in the literature.