Discrete optimization
Unsupervised learning by probabilistic latent semantic analysis
Machine Learning
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
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
Automatic Linguistic Indexing of Pictures by a Statistical Modeling Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Journal of Machine Learning Research
Multi-labelled classification using maximum entropy method
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Correlated Label Propagation with Application to Multi-label Learning
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
An adaptive graph model for automatic image annotation
MIR '06 Proceedings of the 8th ACM international workshop on Multimedia information retrieval
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Dual cross-media relevance model for image annotation
Proceedings of the 15th international conference on Multimedia
A graph-based image annotation framework
Pattern Recognition Letters
Hybrid Generative-Discriminative Visual Categorization
International Journal of Computer Vision
Image annotation via graph learning
Pattern Recognition
International Journal of Computer Vision
Manifold-ranking based topic-focused multi-document summarization
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Multiple Bernoulli relevance models for image and video annotation
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
CBSA: content-based soft annotation for multimodal image retrieval using Bayes point machines
IEEE Transactions on Circuits and Systems for Video Technology
Multi-label learning by Image-to-Class distance for scene classification and image annotation
Proceedings of the ACM International Conference on Image and Video Retrieval
Context dependent SVMs for interconnected image network annotation
Proceedings of the international conference on Multimedia
Automatic image tagging via category label and web data
Proceedings of the international conference on Multimedia
Context-based support vector machines for interconnected image annotation
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part I
Ensemble approach based on conditional random field for multi-label image and video annotation
MM '11 Proceedings of the 19th ACM international conference on Multimedia
MM '11 Proceedings of the 19th ACM international conference on Multimedia
MLRank: Multi-correlation Learning to Rank for image annotation
Pattern Recognition
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This paper presents a novel context-based keyword propagation method for automatic image annotation. We follow the idea of keyword propagation and formulate image annotation as a multi-label learning problem, which is further resolved efficiently by linear programming. In this way, our method can exploit the context between keywords during keyword propagation. Unlike the popular relevance models that treat each keyword independently, our method can simultaneously propagate multiple keywords (i.e. labels) from the training images to the test images using their similarities. Moreover, we present a new 2D string kernel, called spatial spectrum kernel, to take into account another type of context when defining the similarity between images for keyword propagation. Each image is first denoted as a 2D sequence of visual keywords which are obtained through dividing images into blocks and then clustering these blocks, and a spatial spectrum kernel is then proposed to measure the 2D sequence similarity based on shared occurrences of s-length 1D subsequences through decomposing each 2D sequence into two parallel 1D sequences (i.e. the row-wise and column-wise ones). That is, we incorporate the context between visual keywords into the similarity between images (i.e. 2D sequences) used for keyword propagation. Experiments on two standard image databases demonstrate that the proposed method for automatic image annotation outperforms the state-of-the-art methods.