Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
An Affine Invariant Interest Point Detector
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
The Journal of Machine Learning Research
On image auto-annotation with latent space models
MULTIMEDIA '03 Proceedings of the eleventh ACM international conference on Multimedia
GCap: Graph-based Automatic Image Captioning
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 9 - Volume 09
Learning hierarchical multi-category text classification models
ICML '05 Proceedings of the 22nd international conference on Machine learning
A survey of methods for image annotation
Journal of Visual Languages and Computing
Using Image Stimuli to Drive fMRI Analysis
Neural Information Processing
Automatic image tagging as a random walk with priors on the canonical correlation subspace
MIR '08 Proceedings of the 1st ACM international conference on Multimedia information retrieval
Automatic image annotation using visual content and folksonomies
Multimedia Tools and Applications
Annotating images and image objects using a hierarchical dirichlet process model
Proceedings of the 9th International Workshop on Multimedia Data Mining: held in conjunction with the ACM SIGKDD 2008
Canonical contextual distance for large-scale image annotation and retrieval
LS-MMRM '09 Proceedings of the First ACM workshop on Large-scale multimedia retrieval and mining
Tagging and retrieving images with co-occurrence models: from corel to flickr
LS-MMRM '09 Proceedings of the First ACM workshop on Large-scale multimedia retrieval and mining
Image annotation and retrieval based on efficient learning of contextual latent space
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
Exploiting tag and word correlations for improved webpage clustering
SMUC '10 Proceedings of the 2nd international workshop on Search and mining user-generated contents
Towards a new reading experience via semantic fusion of text and music
Proceedings of the 11th annual international ACM/IEEE joint conference on Digital libraries
Knowledge propagation in large image databases using neighborhood information
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Leveraging Social Bookmarks from Partially Tagged Corpus for Improved Web Page Clustering
ACM Transactions on Intelligent Systems and Technology (TIST)
An interactive semi-supervised approach for automatic image annotation
PCM'12 Proceedings of the 13th Pacific-Rim conference on Advances in Multimedia Information Processing
Multi-Label Classification Method for Multimedia Tagging
International Journal of Multimedia Data Engineering & Management
Annotation propagation in image databases using similarity graphs
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Framing image description as a ranking task: data, models and evaluation metrics
Journal of Artificial Intelligence Research
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The automatic annotation of images presents a particularly complex problem for machine learning researchers. In this work we experiment with semantic models and multi-class learning for the automatic annotation of query images. We represent the images using scale invariant transformation descriptors in order to account for similar objects appearing at slightly different scales and transformations. The resulting descriptors are utilised as visual terms for each image. We first aim to annotate query images by retrieving images that are similar to the query image. This approach uses the analogy that similar images would be annotated similarly as well. We then propose an image annotation method that learns a direct mapping from image descriptors to keywords. We compare the semantic based methods of Latent Semantic Indexing and Kernel Canonical Correlation Analysis (KCCA), as well as using a recently proposed vector label based learning method known as Maximum Margin Robot.