Support vector machine active learning for image retrieval
MULTIMEDIA '01 Proceedings of the ninth ACM international conference on Multimedia
Relevance Feedback Decision Trees in Content-Based Image Retrieval
CBAIVL '00 Proceedings of the IEEE Workshop on Content-based Access of Image and Video Libraries (CBAIVL'00)
Efficient Query Refinement for Image Retrieval
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
International Journal of Computer Vision - Special Issue on Content-Based Image Retrieval
Convex Optimization
Retrieval of difficult image classes using svd-based relevance feedback
Proceedings of the 6th ACM SIGMM international workshop on Multimedia information retrieval
Learning a Mahalanobis Metric from Equivalence Constraints
The Journal of Machine Learning Research
Enhancing relevance feedback in image retrieval using unlabeled data
ACM Transactions on Information Systems (TOIS)
Viewpoint Invariant Pedestrian Recognition with an Ensemble of Localized Features
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Semisupervised SVM batch mode active learning with applications to image retrieval
ACM Transactions on Information Systems (TOIS)
A Statistical Framework for Image Category Search from a Mental Picture
IEEE Transactions on Pattern Analysis and Machine Intelligence
Generalized Risk Zone: Selecting Observations for Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Distance Metric Learning for Large Margin Nearest Neighbor Classification
The Journal of Machine Learning Research
IEEE Transactions on Image Processing
Track based relevance feedback for tracing persons in surveillance videos
Computer Vision and Image Understanding
People reidentification in surveillance and forensics: A survey
ACM Computing Surveys (CSUR)
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We address the problem of interactive search for a target of interest in surveillance imagery. Our solution consists of iteratively learning a distance metric for retrieval, based on user feedback. The approach employs (retrieval) rank based constraints and convex optimization to efficiently learn the distance metric. The algorithm uses both user labeled and unlabeled examples in the learning process. The method is fast enough for a new metric to be learned interactively for each target query. In order to reduce the burden on the user, a model-independent active learning method is used to select key examples, for response solicitation. This leads to a significant reduction in the number of user-interactions required for retrieving the target of interest. The proposed method is evaluated on challenging pedestrian and vehicle data sets, and compares favorably to the state of the art in target re-acquisition algorithms.