A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Liberal relevance criteria of TREC -: counting on negligible documents?
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
A General Framework for Object Detection
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Implicit feedback for inferring user preference: a bibliography
ACM SIGIR Forum
An efficient boosting algorithm for combining preferences
The Journal of Machine Learning Research
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Training algorithms for fuzzy support vector machines with noisy data
Pattern Recognition Letters
Evaluation campaigns and TRECVid
MIR '06 Proceedings of the 8th ACM international workshop on Multimedia information retrieval
A regression framework for learning ranking functions using relative relevance judgments
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Journal of the American Society for Information Science and Technology
Cheap and fast---but is it good?: evaluating non-expert annotations for natural language tasks
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Gender Classification Based on Support Vector Machine with Automatic Confidence
ICONIP '09 Proceedings of the 16th International Conference on Neural Information Processing: Part I
Binary and graded relevance in IR evaluations-Comparison of the effects on ranking of IR systems
Information Processing and Management: an International Journal
Saliency moments for image categorization
Proceedings of the 1st ACM International Conference on Multimedia Retrieval
IEEE Transactions on Neural Networks
Exploring two spaces with one feature: kernelized multidimensional modeling of visual alphabets
Proceedings of the 2nd ACM International Conference on Multimedia Retrieval
Where is the beauty?: retrieving appealing VideoScenes by learning Flickr-based graded judgments
Proceedings of the 20th ACM international conference on Multimedia
Semantic indexing and computational aesthetics: interactions, bridgesand boundaries
Proceedings of the 3rd ACM conference on International conference on multimedia retrieval
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Traditional Content Based Multimedia Retrieval (CBMR) systems measure the relevance of visual samples using a binary scale (Relevant/Non Relevant). However, a picture can be relevant to a semantic category with different degrees, depending on the way such concept is represented in the image. In this paper, we build a CBMR framework that supports graded relevance judgments. In order to quickly build graded ground truths, we propose a measure to reassess binary-labeled databases without involving manual effort: we automatically assign a reliable relevance degree (Non, Weakly, Average, Very Relevant) to each sample, based on its position with respect to the hyperplane drawn by support vector machines in the feature space. We test the effectiveness of our system on two large-scale databases, and we show that our approach outperforms the traditional binary relevance-based frameworks in both scene recognition and video retrieval.