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In this paper, we study a classification problem in which sample labels are randomly corrupted. In this scenario, there is an unobservable sample with noise-free labels. However, before being observed, the true labels are independently flipped with a probability $\rho \in [0,0.5)$ , and the random label noise can be class-conditional. Here, we address two fundamental problems raised by this scenario. The first is how to best use the abundant surrogate loss functions designed for the traditional classification problem when there is label noise. We prove that any surrogate loss function can be used for classification with noisy labels by using importance reweighting, with consistency assurance that the label noise does not ultimately hinder the search for the optimal classifier of the noise-free sample. The other is the open problem of how to obtain the noise rate \rho$ . We show that the rate is upper bounded by the conditional probability P(\hat{Y}|X) of the noisy sample. Consequently, the rate can be estimated, because the upper bound can be easily reached in classification problems. Experimental results on synthetic and real datasets confirm the efficiency of our methods. [ABSTRACT FROM PUBLISHER]

We describe an algorithm for the efficient annotation of events of interest in video microscopy. The specific application involves the detection and tracking of multiple possibly overlapping vesicles in total internal reflection fluorescent microscopy images. A statistical model for the dynamic image data of vesicle configurations allows us to properly weight various hypotheses online. The goal is to find the most likely trajectories given a sequence of images. The computational challenge is addressed by defining a sequence of coarse-to-fine tests, derived from the statistical model, to quickly eliminate most candidate positions at each time frame. The computational load of the tests is initially very low and gradually increases as the false positives become more difficult to eliminate. Only at the last step are state variables estimated from a complete time-dependent model. Processing time thus mainly depends on the number of vesicles in the image and not on image size. [ABSTRACT FROM AUTHOR]