Practical methods of optimization; (2nd ed.)
Practical methods of optimization; (2nd ed.)
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Reducing bias in curve estimation by use of weights
Computational Statistics & Data Analysis
Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
SIMPLIcity: Semantics-Sensitive Integrated Matching for Picture LIbraries
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Region-Based Fuzzy Feature Matching Approach to Content-Based Image Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Object Recognition as Machine Translation: Learning a Lexicon for a Fixed Image Vocabulary
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
Content-Based Image Retrieval Using Multiple-Instance Learning
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Automatic Linguistic Indexing of Pictures by a Statistical Modeling Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Journal of Machine Learning Research
The Journal of Machine Learning Research
An Improved Cluster Labeling Method for Support Vector Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Novel Kernel Method for Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Large-Scale Concept Ontology for Multimedia
IEEE MultiMedia
Dynamic Characterization of Cluster Structures for Robust and Inductive Support Vector Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Supervised Learning of Semantic Classes for Image Annotation and Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image retrieval: Ideas, influences, and trends of the new age
ACM Computing Surveys (CSUR)
Semantic content analysis and annotation of histological images
Computers in Biology and Medicine
Real-Time Computerized Annotation of Pictures
IEEE Transactions on Pattern Analysis and Machine Intelligence
Annotating Images by Mining Image Search Results
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic Semantic Annotation of Real-World Web Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image annotation via graph learning
Pattern Recognition
A Bayesian network-based framework for semantic image understanding
Pattern Recognition
A Hybrid Probabilistic Model for Unified Collaborative and Content-Based Image Tagging
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image Annotation by Graph-Based Inference With Integrated Multiple/Single Instance Representations
IEEE Transactions on Multimedia
A new kernel-based fuzzy clustering approach: support vector clustering with cell growing
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Image Processing
Structured Max-Margin Learning for Inter-Related Classifier Training and Multilabel Image Annotation
IEEE Transactions on Image Processing
An overview of statistical learning theory
IEEE Transactions on Neural Networks
Spatial Markov Kernels for Image Categorization and Annotation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Effective Semantic Annotation by Image-to-Concept Distribution Model
IEEE Transactions on Multimedia
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Continual progress in the fields of computer vision and machine learning has provided opportunities to develop automatic tools for tagging images; this facilitates searching and retrieving. However, due to the complexity of real-world image systems, effective and efficient image annotation is still a challenging problem. In this paper, we present an annotation technique based on the use of image content and word correlations. Clusters of images with manually tagged words are used as training instances. Images within each cluster are modeled using a kernel method, in which the image vectors are mapped to a higher-dimensional space and the vectors identified as support vectors are used to describe the cluster. To measure the extent of the association between an image and a model described by support vectors, the distance from the image to the model is computed. A closer distance indicates a stronger association. Moreover, word-to-word correlations are also considered in the annotation framework. To tag an image, the system predicts the annotation words by using the distances from the image to the models and the word-to-word correlations in a unified probabilistic framework. Simulated experiments were conducted on three benchmark image data sets. The results demonstrate the performance of the proposed technique, and compare it to the performance of other recently reported techniques.