New trends and ideas in visual concept detection: the MIR flickr retrieval evaluation initiative
Proceedings of the international conference on Multimedia information retrieval
The segmented and annotated IAPR TC-12 benchmark
Computer Vision and Image Understanding
IEEE Transactions on Pattern Analysis and Machine Intelligence
Evaluating Color Descriptors for Object and Scene Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Leveraging loosely-tagged images and inter-object correlations for tag recommendation
Proceedings of the international conference on Multimedia
Leveraging social media for scalable object detection
Pattern Recognition
Large-scale live active learning: Training object detectors with crawled data and crowds
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Multi-modal region selection approach for training object detectors
Proceedings of the 2nd ACM International Conference on Multimedia Retrieval
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Motivated by the abundant availability of user-generated multimedia content, a data augmentation approach that enhances an initial manually labelled training set with regions from user tagged images is presented. Initially, object detection classifiers are trained using a small number of manually labelled regions as the training set. Then, a set of positive regions is automatically selected from a large number of loosely tagged images, pre-segmented by an automatic segmentation algorithm, to enhance the initial training set. In order to overcome the noisy nature of user tagged images and the lack of information about the pixel level annotations, the main contribution of this work is the introduction of the visual ambiguity term. Visual ambiguity is caused by the visual similarity of semantically dissimilar concepts with respect to the employed visual representation and analysis system (i.e. segmentation, feature space, classifier) and, in this work, is modelled so that the images where ambiguous concepts co-exist are penalized. Preliminary experimental results show that the employment of visual ambiguity guides the selection process away from the ambiguous images and, as a result, allows for better separation between the targeted true positive and the undesired negative regions.