CIVR '08 Proceedings of the 2008 international conference on Content-based image and video retrieval
Expert Systems with Applications: An International Journal
Style modeling for tagging personal photo collections
Proceedings of the ACM International Conference on Image and Video Retrieval
Improved Resulted Word Counts Optimizer for Automatic Image Annotation Problem
Fundamenta Informaticae - Advances in Artificial Intelligence and Applications
Proceedings of the international conference on Multimedia information retrieval
MAP-based image tag recommendation using a visual folksonomy
Pattern Recognition Letters
Image annotation by incorporating word correlations into multi-class SVM
ICNC'09 Proceedings of the 5th international conference on Natural computation
Topic models for image annotation and text illustration
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Automatic image search based on improved feature descriptors and decision tree
Integrated Computer-Aided Engineering
Effective term weighting in ALT text prediction for web image retrieval
APWeb'11 Proceedings of the 13th Asia-Pacific web conference on Web technologies and applications
Using manual and automated annotations to search images by semantic similarity
Multimedia Tools and Applications
An annotation rule extraction algorithm for image retrieval
Pattern Recognition Letters
Web image annotation using an effective term weighting
CICLing'12 Proceedings of the 13th international conference on Computational Linguistics and Intelligent Text Processing - Volume Part II
Improved Resulted Word Counts Optimizer for Automatic Image Annotation Problem
Fundamenta Informaticae - Advances in Artificial Intelligence and Applications
Apples to oranges: evaluating image annotations from natural language processing systems
NAACL HLT '12 Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
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The Corel Image set is widely used for image annotation performance evaluation although it has been claimed that Corel images are relatively easy to annotate. The aim of this paper is to demonstrate some of the disadvantages of datasets like the Corel set for effective auto-annotation evaluation. We first compare the performance of several annotation algorithms using the Corel set and find that simple near neighbor propagation techniques perform fairly well. A support vector machine (SVM)-based annotation method achieves even better results, almost as good as the best found in the literature. We then build a new image collection using the Yahoo Image Search engine and query-by-single-word searches to create a more challenging annotated set automatically. Then, using three very different image annotation methods, we demonstrate some of the problems of annotation using the Corel set compared with the Yahoo-based training set. In both cases the training sets are used to create a set of annotations for the Corel test set