Centroid

About: Centroid is a research topic. Over the lifetime, 4110 publications have been published within this topic receiving 53637 citations. The topic is also known as: barycenter (geometry) & geometric center of a plane figure.

A New Centroid-Based Classifier for Text Categorization

Abstract: In recent years, centroid-based document classifiers receive wide interests from text mining community because of their simplicity and linear-time complexity. However, the traditional centroid-based classifiers usually perform less effectively for Chinese text categorization. In this paper, we tackle the problem by developing a new way to calculate the class-specific weights for each term in the training phase; in the testing phase, the new documents are assigned to the centroid to which the document is most similar based on the weighted distance measurement. The experimental results demonstrate that the accuracy of our algorithm outperforms the traditional centroid-based classifiers, as well as outstanding efficiency compared with the Support Vector Machine (SVM) based classifiers for Chinese text categorization.

Balanced Open Set Domain Adaptation via Centroid Alignment

Abstract: Open Set Domain Adaptation (OSDA) is a challenging domain adaptation setting which allows the existence of unknown classes on the target domain. Although existing OSDA methods are good at classifying samples of known classes, they ignore the classification ability for the unknown samples, making them unbalanced OSDA methods. To alleviate this problem, we propose a balanced OSDA methods which could recognize the unknown samples while maintain high classification performance for the known samples. Specifically, to reduce the domain gaps, we first project the features to a hyperspherical latent space. In this space, we propose to bound the centroid deviation angles to not only increase the intra-class compactness but also enlarge the inter-class margins. With the bounded centroid deviation angles, we employ the statistical Extreme Value Theory to recognize the unknown samples that are misclassified into known classes. In addition, to learn better centroids, we propose an improved centroid update strategy based on sample reweighting and adaptive update rate to cooperate with centroid alignment. Experimental results on three OSDA benchmarks verify that our method can significantly outperform the compared methods and reduce the proportion of the unknown samples being misclassified into known classes.

Accurate and Robust Synchronous Extraction Algorithm for Star Centroid and Nearby Celestial Body Edge

Abstract: Celestial body features are important navigation information in deep space exploration. This study provides a synchronous high-precision extraction algorithm for star centroid and nearby celestial body edges for a miniaturized independent optical navigation sensor, which combines the functions of a star tracker and a navigation camera. The image is filtered by a ring filter template to eliminate the interference information of background and improve the contrast between the target and the background. The second-order directional derivative and specific area characteristic method aim to roughly extract and distinguish the features (star centroid and the nearby celestial body edge). In local area template where feature points are located, the 1D energy deviation effect is proposed to extract the features of the two different light intensity distribution models. The accuracy and robustness of our algorithm are verified by simulation and ground-based experiments. The algorithm has certain reference significance for other types of dim target and edge detections, such as infrared detection, medical image, target measurement, and machine vision.

Motion tracking based on area and level set weighted centroid shifting

Abstract: In this study, the authors propose a stable colour-based tracking algorithm based on a new representation of the target location: the area weighted mean of the centroids corresponding to each colour bin of the target. The target location is well discriminated, since the centroids contain spatial information on the distribution of the colours and are rather insensitive to the loss of pixels and change in the number of pixels. The area weighting takes care that the major colours are treated with more importance than the minor colours. Due to these properties, it is possible to track the target in difficult conditions such as low-frame-rate environment, severe partial occlusion and partial colour change environment. Furthermore, the target localisation can be achieved in a one-step computation, which makes the algorithm fast. The authors compare the stability of the proposed tracking scheme with the original mean shift based tracker, both mathematically and experimentally. They also propose a background feature elimination algorithm, which is based on the level set based bimodal segmentation. The level set based bimodal segmentation segments out the region with dominant background feature and thus increases the robustness of the scheme.