Weighted Mean Shift Research Articles

Automated Clustering of High-dimensional Data with a Feature Weighted Mean Shift Algorithm

Mean shift is a simple interactive procedure that gradually shifts data points towards the mode which denotes the highest density of data points in the region. Mean shift algorithms have been effectively used for data denoising, mode seeking, and finding the number of clusters in a dataset in an automated fashion. However, the merits of mean shift quickly fade away as the data dimensions increase and only a handful of features contain useful information about the cluster structure of the data. We propose a simple yet elegant feature-weighted variant of mean shift to efficiently learn the feature importance and thus, extending the merits of mean shift to high-dimensional data. The resulting algorithm not only outperforms the conventional mean shift clustering procedure but also preserves its computational simplicity. In addition, the proposed method comes with rigorous theoretical convergence guarantees and a convergence rate of at least a cubic order. The efficacy of our proposal is thoroughly assessed through experimental comparison against baseline and state-of-the-art clustering methods on synthetic as well as real-world datasets.

Radar group target recognition based on HRRPs and weighted mean shift clustering

When range high-resolution radar is applied to target recognition, it is quite possible for the high-resolution range profiles (HRRPs) of group targets in a beam to overlap, which reduces the target recognition performance of the radar. In this paper, we propose a group target recognition method based on a weighted mean shift (weighted-MS) clustering method. During the training phase, subtarget features are extracted based on the template database, which is established through simulation or data acquisition, and the features are fed to the support vector machine (SVM) classifier to obtain the classifier parameters. In the test phase, the weighted-MS algorithm is exploited to extract the HRRP of each subtarget. Then, the features of the subtarget HRRP are extracted and used as input in the SVM classifier to be recognized. Compared to the traditional group target recognition method, the proposed method has the advantages of requiring only a small amount of computation, setting parameters automatically, and having no requirement for target motion. The experimental results based on the measured data show that the method proposed in this paper has better recognition performance and is more robust against noise than other recognition methods.

An object tracking method based on Mean Shift algorithm with HSV color space and texture features

Mean Shift is a powerful and versatile non-parametric iterative algorithm that can be used for lot of purposes like finding modes, clustering etc. It has been widely used in target tracking field because of some advantages like fewer iteration times and better real-time performance for many years. However, due to only single-color histogram representation of target feature has been used in traditional Mean Shift algorithm, it cannot track very well in some cases, especially under very complicated conditions. There are mainly two problems that can cause traditional Mean Shift algorithm to be unstable. The first problem is when the background color and target color are similar, the tracking performance is significantly insufficient, the second is the partial occlusion problem. In this paper, we have proposed a solution to solve these two issues, which contains three improvements. For the first problem, we transformed original color features in traditional Mean Shift algorithm into HSV color space, At the same time, we will also consider texture features and integrate into algorithm to improve tracking performance. and, we applied four neighborhood searching method to solve partial occlusion problem. We tested our algorithms on a variety of standard datasets and a video data collected from real-world environment. The result of experiments show that our proposed algorithm has higher accuracy than traditional Mean Shift algorithm and the background weighted Mean Shift algorithm in test case of complex conditions. Besides, our proposed algorithm also has a good operating efficiency then traditional one.

Line detection algorithm based on adaptive gradient threshold and weighted mean shift

Line detection is a classical problem in computer vision and image processing, and it is widely used as a basic method. Most of existing line detection algorithms are based on edge information, whose discontinuity limited the detection result. Meanwhile, some other algorithms only use gradient magnitudes, and neglect the function of gradient directions. In this paper, an adaptive gradient threshold and omni-direction line growing method based on line detection with weighted mean shift procedure and 2D slice sampling strategy (referred to as LSWMSAllDir) is proposed. It makes full use of the magnitudes and directions of the gradient to detect lines in the image. Experiments on synthetic data and real scene image data showed that the improve algorithm was the most accurate when compared with Progressive Probabilistic Hough Transform (PPHT), line segment detector (LSD), parameter free edge drawing (EDPF) and original line segment detection using weighted mean shift (LSWMS) algorithms.

High-Throughput Biomarker Segmentation on Ovarian Cancer Tissue Microarrays via Hierarchical Normalized Cuts

We present a system for accurately quantifying the presence and extent of stain on account of a vascular biomarker on tissue microarrays. We demonstrate our flexible, robust, accurate, and high-throughput minimally supervised segmentation algorithm, termed hierarchical normalized cuts (HNCuts) for the specific problem of quantifying extent of vascular staining on ovarian cancer tissue microarrays. The high-throughput aspect of HNCut is driven by the use of a hierarchically represented data structure that allows us to merge two powerful image segmentation algorithms-a frequency weighted mean shift and the normalized cuts algorithm. HNCuts rapidly traverses a hierarchical pyramid, generated from the input image at various color resolutions, enabling the rapid analysis of large images (e.g., a 1500 × 1500 sized image under 6 s on a standard 2.8-GHz desktop PC). HNCut is easily generalizable to other problem domains and only requires specification of a few representative pixels (swatch) from the object of interest in order to segment the target class. Across ten runs, the HNCut algorithm was found to have average true positive, false positive, and false negative rates (on a per pixel basis) of 82%, 34%, and 18%, in terms of overlap, when evaluated with respect to a pathologist annotated ground truth of the target region of interest. By comparison, a popular supervised classifier (probabilistic boosting trees) was only able to marginally improve on the true positive and false negative rates (84% and 14%) at the expense of a higher false positive rate (73%), with an additional computation time of 62% compared to HNCut. We also compared our scheme against a k-means clustering approach, which both the HNCut and PBT schemes were able to outperform. Our success in accurately quantifying the extent of vascular stain on ovarian cancer TMAs suggests that HNCut could be a very powerful tool in digital pathology and bioinformatics applications where it could be used to facilitate computer-assisted prognostic predictions of disease outcome.

Line segment detection using weighted mean shift procedures on a 2D slice sampling strategy

A new line segment detection approach is introduced in this paper for its application in real-time computer vision systems. It has been designed to work unsupervised without any prior knowledge of the imaged scene; hence, it does not require tuning of input parameters. Although many works have been presented on this topic, as far as we know, none of them achieves a trade-off between accuracy and speed as our strategy does. The reduction of the computational cost compared to other fast methods is based on a very efficient sampling strategy that sequentially proposes points on the image that likely belong to line segments. Then, a fast line growing algorithm is applied based on the Bresenham algorithm, which is combined with a modified version of the mean shift algorithm to provide accurate line segments while being robust against noise. The performance of this strategy is tested for a wide variety of images, comparing its results with popular state-of-the-art line segment detection methods. The results show that our proposal outperforms these works considering simultaneously accuracy in the results and processing speed.