Image segmentation is a key step in image processing tasks, which has significant applications in computer vision field such as medical image analysis, scene understanding and video monitoring, etc. However, image seg...
详细信息
Image segmentation is a key step in image processing tasks, which has significant applications in computer vision field such as medical image analysis, scene understanding and video monitoring, etc. However, image segmentation tasks usually require a large number of labeled samples to obtain great performance of convolutional neural networks (CNNs). Active learning (AL) can select valuable samples for annotation, so as to reduce the annotation cost as much as possible while maintaining the performance of CNNs. Further, one-shot AL can select valuable samples by once, which eliminates the need for iterative sample selection and annotation. However, existing one-shot AL approaches extremely rely on complex clustering algorithm, which brings a limitation in practice, i.e., we often do not know how to set the hyperparameters. In this paper, we propose a clustering-free one-shot AL framework, which is based on self-supervised feature learning and density-based query strategy. Our framework can select samples with high local density robustly against hyperparameters. The experimental results are impressive that state-of-the-art one-shot active learning performance can be achieved with simple density-based sampling.
With precipitously growing demand to detect outliers in data streams, many studies have been conducted aiming to develop extensions of well-known outlier detection algorithm called Local Outlier Factor (LOF), for data...
详细信息
ISBN:
(纸本)9781450355520
With precipitously growing demand to detect outliers in data streams, many studies have been conducted aiming to develop extensions of well-known outlier detection algorithm called Local Outlier Factor (LOF), for data streams. However, existing LOF-based algorithms for data streams still suffer from two inherent limitations: 1) Large amount of memory space is required. 2) A long sequence of outliers is not detected. In this paper, we propose a new outlier detection algorithm for data streams, called DILOF that effectively overcomes the limitations. To this end, we first develop a novel density-based sampling algorithm to summarize past data and then propose a new strategy for detecting a sequence of outliers. It is worth noting that our proposing algorithms do not require any prior knowledge or assumptions on data distribution. Moreover, we accelerate the execution time of DILOF about 15 times by developing a powerful distance approximation technique. Our comprehensive experiments on real-world datasets demonstrate that DILOF significantly outperforms the state-of-the-art competitors in terms of accuracy and execution time. The source code for the proposed algorithm is pavailable at our website: http://***/DILOF.
暂无评论