The anatomy of a large-scale hypertextual Web search engine
WWW7 Proceedings of the seventh international conference on World Wide Web 7
Unsupervised learning by probabilistic latent semantic analysis
Machine Learning
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
A Bayesian Hierarchical Model for Learning Natural Scene Categories
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Multi-label informed latent semantic indexing
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
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
Fast Random Walk with Restart and Its Applications
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
LabelMe: A Database and Web-Based Tool for Image Annotation
International Journal of Computer Vision
Extracting shared subspace for multi-label classification
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Multi-label feature transform for image classifications
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
Multi-label linear discriminant analysis
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part VI
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
Sharing features between objects and their attributes
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Image annotation using bi-relational graph of images and semantic labels
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Function-Function correlated multi-label protein function prediction over interaction networks
RECOMB'12 Proceedings of the 16th Annual international conference on Research in Computational Molecular Biology
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Image annotation as well as classification are both critical and challenging work in computer vision research. Due to the rapid increasing number of images and inevitable biased annotation or classification by the human curator, it is desired to have an automatic way. Recently, there are lots of methods proposed regarding image classification or image annotation. However, people usually treat the above two tasks independently and tackle them separately. Actually, there is a relationship between the image class label and image annotation terms. As we know, an image with the sport class label rowing is more likely to be annotated with the terms water, boat and oar than the terms wall, net and floor, which are the descriptions of indoor sports. In this paper, we propose a new method for jointly class recognition and terms annotation. We present a novel Tri-Relational Graph (TG) model that comprises the data graph, annotation terms graph, class label graph, and connect them by two additional graphs induced from class label as well as annotation assignments. Upon the TG model, we introduce a Biased Random Walk (BRW) method to jointly recognize class and annotate terms by utilizing the interrelations between two tasks. We conduct the proposed method on two benchmark data sets and the experimental results demonstrate our joint learning method can achieve superior prediction results on both tasks than the state-of-the-art methods.