The nature of statistical learning theory
The nature of statistical learning theory
WordNet: a lexical database for English
Communications of the ACM
Machine Learning
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Support vector machine active learning for image retrieval
MULTIMEDIA '01 Proceedings of the ninth ACM international conference on Multimedia
Building Text Classifiers Using Positive and Unlabeled Examples
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Negative pseudo-relevance feedback in content-based video retrieval
MULTIMEDIA '03 Proceedings of the eleventh ACM international conference on Multimedia
Robust Real-Time Face Detection
International Journal of Computer Vision
Learning the semantics of multimedia queries and concepts from a small number of examples
Proceedings of the 13th annual ACM international conference on Multimedia
Probabilistic web image gathering
Proceedings of the 7th ACM SIGMM international workshop on Multimedia information retrieval
Large-Scale Concept Ontology for Multimedia
IEEE MultiMedia
To search or to label?: predicting the performance of search-based automatic image classifiers
MIR '06 Proceedings of the 8th ACM international workshop on Multimedia information retrieval
The Google Similarity Distance
IEEE Transactions on Knowledge and Data Engineering
The class imbalance problem: A systematic study
Intelligent Data Analysis
LabelMe: A Database and Web-Based Tool for Image Annotation
International Journal of Computer Vision
Speeded-Up Robust Features (SURF)
Computer Vision and Image Understanding
Annotating Images by Mining Image Search Results
IEEE Transactions on Pattern Analysis and Machine Intelligence
80 Million Tiny Images: A Large Data Set for Nonparametric Object and Scene Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Visual categorization with negative examples for free
MM '09 Proceedings of the 17th ACM international conference on Multimedia
NUS-WIDE: a real-world web image database from National University of Singapore
Proceedings of the ACM International Conference on Image and Video Retrieval
Learning social tag relevance by neighbor voting
IEEE Transactions on Multimedia
Learning automatic concept detectors from online video
Computer Vision and Image Understanding
On the sampling of web images for learning visual concept classifiers
Proceedings of the ACM International Conference on Image and Video Retrieval
Image tag refinement towards low-rank, content-tag prior and error sparsity
Proceedings of the international conference on Multimedia
Quantifying tag representativeness of visual content of social images
Proceedings of the international conference on Multimedia
Content-based tag processing for Internet social images
Multimedia Tools and Applications
Harvesting Image Databases from the Web
IEEE Transactions on Pattern Analysis and Machine Intelligence
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Real-Time Visual Concept Classification
IEEE Transactions on Multimedia
On the pooling of positive examples with ontology for visual concept learning
MM '11 Proceedings of the 19th ACM international conference on Multimedia
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Fusing concept detection and geo context for visual search
Proceedings of the 2nd ACM International Conference on Multimedia Retrieval
Multi-modal region selection approach for training object detectors
Proceedings of the 2nd ACM International Conference on Multimedia Retrieval
Proceedings of the 2012 international workshop on Socially-aware multimedia
Machine Vision and Applications
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To learn classifiers for many visual categories, obtaining labeled training examples in an efficient way is crucial. Since a classifier tends to misclassify negative examples which are visually similar to positive examples, inclusion of such informative negatives should be stressed in the learning process. However, they are unlikely to be hit by random sampling, the de facto standard in literature. In this paper, we go beyond random sampling by introducing a novel social negative bootstrapping approach. Given a visual category and a few positive examples, the proposed approach adaptively and iteratively harvests informative negatives from a large amount of social-tagged images. To label negative examples without human interaction, we design an effective virtual labeling procedure based on simple tag reasoning. Virtual labeling, in combination with adaptive sampling, enables us to select the most misclassified negatives as the informative samples. Learning from the positive set and the informative negative sets results in visual classifiers with higher accuracy. Experiments on two present-day image benchmarks employing 650K virtually labeled negative examples show the viability of the proposed approach. On a popular visual categorization benchmark our precision at 20 increases by 34%, compared to baselines trained on randomly sampled negatives. We achieve more accurate visual categorization without the need of manually labeling any negatives.