基于改进时空兴趣点特征的双人交互行为识别

针对实际监控视频下双人交互行为的兴趣点特征选取不理想,且聚类词典中冗余单词导致识别率不高的问题,提出一种基于改进时空兴趣点（STIP）特征的交互行为识别方法。首先,引入基于信息熵的不可跟踪性检测方法,对序列图像进行跟踪得到交互动作的前景运动区域,仅在此区域内提取时空兴趣点以提高兴趣点检测的准确性。其次采用3维尺度不变特性转换（3D-SIFT）描述子对检测得到的兴趣点进行表述,利用改进的模糊C均值聚类方法得到视觉词典,以提升词典的分布特性;在此基础上建立词袋模型,即将训练集样本向词典进行投影得到每帧图像的直方图统计特征表示。最后,采用帧帧最近邻分类方法进行双人交互动作识别。在UT-interaction数据库上进行测试,该算法得到了91.7%的正确识别率。实验结果表明,通过不可跟踪性检测得到的时空兴趣点的改进词袋算法可以较大程度提高交互行为识别的准确率,并且适用于动态背景下的双人交互行为识别。     

Track color space-time interest points in video

Multimedia Tools and Applications - Color Space-Time Interest Points (CSTIP) are among all the interesting low-level features which can be extracted from videos; they provide an efficient...     

图像识别中的兴趣点匹配方法研究

针对图像检索识别的需求,提出了一种基于兴趣点的匹配算法,利用小波变换对图像进行降维和去噪,提取其SIFT点特征,同时进行PCA降维,最后采用基于K-d树的最近邻法进行快速匹配。通过对各种图像大量的实验,结果表明,该方法具有很强的匹配性和鲁棒性,是一种较好的图像匹配算法,可以广泛应用于图像的检索和识别中。     

On recommendation problems beyond points of interest

Recommendation systems aim to recommend items or packages of items that are likely to be of interest to users. Previous work on recommendation systems has mostly focused on recommending points of interest (POI), to identify and suggest top-k items or packages that meet selection criteria and satisfy compatibility constraints on items in a package, where the (packages of) items are ranked by their usefulness to the users. As opposed to prior work, this paper investigates two issues beyond POI recommendation that are also important to recommendation systems. When there exist no sufficiently many POI that can be recommended, we propose (1) query relaxation recommendation to help users revise their selection criteria, or (2) adjustment recommendation to guide recommendation systems to modify their item collections, such that the users׳ requirements can be satisfied.We study two related problems, to decide (1) whether the query expressing the selection criteria can be relaxed to a limited extent, and (2) whether we can update a bounded number of items, such that the users can get desired recommendations. We establish the upper and lower bounds of these problems, all matching, for both combined and data complexity, when selection criteria and compatibility constraints are expressed in a variety of query languages, for both item recommendation and package recommendation. To understand where the complexity comes from, we also study the impact of variable sizes of packages, compatibility constraints and selection criteria on the analyses of these problems. Our results indicate that in most cases the complexity bounds of query relaxation and adjustment recommendation are comparable to their counterparts of the basic recommendation problem for testing whether a given set of (resp. packages of) items makes top-k items (resp. packages). In other words, extending recommendation systems with the query relaxation and adjustment recommendation functionalities typically does not incur extra overhead.     

Invariant Gabor-based interest points detector under geometric transformation

This paper proposes a novel approach for detecting interest points invariant to rotation and scale using Gabor wavelet. Our scale and rotational invariant interest points detector is produced based on a multi-scale representation by selecting points at different scales from a combination of multi-orientations response maps using a non-maximum suppressing technique. We present a comparative evaluation of our proposed detector with the existing detectors. Experimental results show that our proposed method outperforms other methods under different viewpoint angles and provides a comparable results to the existing methods under scale changes for a set of test images with different geometric transformations.     

Fast radial symmetry for detecting points of interest

A new transform is presented that utilizes local radial symmetry to highlight points of interest within a scene. Its low-computational complexity and fast runtimes makes this method well-suited for real-time vision applications. The performance of the transform is demonstrated on a wide variety of images and compared with leading techniques from the literature. Both as a facial feature detector and as a generic region of interest detector the new transform is seen to offer equal or superior performance to contemporary techniques at a relatively low-computational cost. A real-time implementation of the transform is presented running at over 60 frames per second on a standard Pentium III PC.     

一种基于兴趣点匹配的图像拼接方法

针对基于图像特征点的配准方法中对应特征对难以准确提取的问题,提出一种基于兴趣点匹配的图像自动拼接方法。该方法首先利用Harris角检测器提取两幅图像中的兴趣点,并在此基础上采用比较最大值法提取出对应兴趣点特征对,最后利用这些匹配特征对来实现图像的拼接。实验结果表明,这种方法能有效地去除伪匹配特征对的干扰,同时降低了误匹配的概率,对于全景图的拼接具有良好的效果。     

基于兴趣点综合特征的图像检索

针对环形区域能更好地表达像素空间分布的特点,将其引入到Gabor小波纹理特征中,提出了一种基于兴趣点环形区域颜色和纹理特征的图像检索算法。首先采用自适应平滑滤波器对图像进行滤波处理,消除噪声的影响并利用快速鲁棒特征(SURF)算子检测兴趣点;然后计算兴趣点周围局部区域内环形颜色直方图及纹理特征,将其作为图像的综合特征;最后根据图像综合特征相似度,输出相似图像。实验结果表明,该算法使平均检索准确率提高至少7％。     

Practical simulation of virtual crowds using points of interest

We present a computational method that exploits points of interest (POIs) to generate realistic virtual pedestrians for a city model, i.e., a simulated crowd. Our method is validated using mobility traces collected longitudinally from a city-wide free and open Wi-Fi network in downtown Oulu, Finland. Analysing this data, we first construct a time-varying Origin–Destination matrix that describes how individual pedestrians in our city move at different times and places. We compare this ground-truth against a random pedestrian model to investigate how the latter underestimates or overestimates movement at various locations or times of day. By identifying these deviations, we can calibrate a weighted model that uses POIs from OpenStreetMap to adjust the simulated crowd. Our results show a significant accuracy improvement over the random model, while at the same time our work is readily applicable to simulating crowds in other cities (real and virtual) as long as POI can be defined spatially.     

Blur robust extremal region-based interest points for medical image registration

Various studies on interest point (IP) detection have concluded that maximally stable extremal region (MSER)-based IPs outperform others on repeatability, localization accuracy, robustness, efficiency and covariance to global and local image distortions. Since medical images lack sharp detail, corner IPs are not a suitable choice for them. Instead, MSERs which offer region-based IPs are useful. However, sensitivity of MSERs to image blur and scale makes them less useful practically. In this context, through this paper, following contributions are made—(1) It is proposed to study MSER-based IPs in Intensity Scale Space instead of conventional Scale Space to better understand and mitigate the problem of IP clutter. (2) By modulating the connectivity of previously proposed ER-based IPs (inspired from visual saliency approach), blur and scale sensitivity of region-based IPs is shown to reduce significantly. The newly developed IPs are called ‘blur robust extremal region (BRER)’ IPs. (3) Owing to the global nature of evaluation parameters (like repeatability) for IP detection, the problem of incorrect judgment is highlighted. As a solution to it, three new evaluation parameters called ‘Uniformity Index,’ ‘10 % core distance’ and ‘Informativeness’ are proposed. These indices capture the idea of uniform distribution of IPs over the entire image, IP clutter and the redundancy of registered IP pairs, respectively. Experiments on database of medical images of different modalities and various organs/diseases suggest that proposed BRER IPs are robust to blur and scale. Also, proposed indices of evaluation offer better judgment of quality of image registration.     

Robust tracking with interest points: A sparse representation approach

Visual tracking is an important task in various computer vision applications including visual surveillance, human computer interaction, event detection, video indexing and retrieval. Recent state of the art sparse representation (SR) based trackers show better robustness than many of the other existing trackers. One of the issues with these SR trackers is low execution speed. The particle filter framework is one of the major aspects responsible for slow execution, and is common to most of the existing SR trackers. In this paper,¹ we propose a robust interest point based tracker in l₁ minimization framework that runs at real-time with performance comparable to the state of the art trackers. In the proposed tracker, the target dictionary is obtained from the patches around target interest points. Next, the interest points from the candidate window of the current frame are obtained. The correspondence between target and candidate points is obtained via solving the proposed l₁ minimization problem.In order to prune the noisy matches, a robust matching criterion is proposed, where only the reliable candidate points that mutually match with target and candidate dictionary elements are considered for tracking. The object is localized by measuring the displacement of these interest points. The reliable candidate patches are used for updating the target dictionary. The performance and accuracy of the proposed tracker is benchmarked with several complex video sequences. The tracker is found to be considerably fast as compared to the reported state of the art trackers. The proposed tracker is further evaluated for various local patch sizes, number of interest points and regularization parameters. The performance of the tracker for various challenges including illumination change, occlusion, and background clutter has been quantified with a benchmark dataset containing 50 videos.     

基于兴趣点和边缘特征的图像检索方法的研究

提出了多种图像特征相结合的图像检索新方法.该方法把兴趣点和边缘作为图像中用户关注的线索,设计了一种基于兴趣点和边缘特征的图像检索新方法.它综合考虑了图像的全局形状特征、颜色及其空间分布信息,不仅克服了单一特征无法真正表征图像的缺陷,同时保证了检索算法对图像旋转、平移的识别不变性.进行检索时,可根据兴趣点的数目自适应地改变兴趣点和边缘特征的相似性权重,增加了系统的有效性.与同类方法相比较表明,该方法有效提高了图像检索的准确率.     

A comprehensive survey of human action recognition with spatio-temporal interest point (STIP) detector

Over the past two decades, human action recognition from video has been an important area of research in computer vision. Its applications include surveillance systems, human–computer interactions and various real-world applications where one of the actor is a human being. A number of review works have been done by several researchers in the context of human action recognition. However, it is found that there is a gap in literature when it comes to methodologies of STIP-based detector for human action recognition. This paper presents a comprehensive review on STIP-based methods for human action recognition. STIP-based detectors are robust in detecting interest points from video in spatio-temporal domain. This paper also summarizes related public datasets useful for comparing performances of various techniques.     

Evolutionary-computer-assisted design of image operators that detect interest points using genetic programming

This work describes a way of designing interest point detectors using an evolutionary-computer-assisted design approach. Nowadays, feature extraction is performed through the paradigm of interest point detection due to its simplicity and robustness for practical applications such as: image matching and view-based object recognition. Genetic programming is used as the core functionality of the proposed human-computer framework that significantly augments the scope of interest point design through a computer assisted learning process. Indeed, genetic programming has produced numerous interest point operators, many with unique or unorthodox designs. The analysis of those best detectors gives us an advantage to achieve a new level of creative design that improves the perspective for human-machine innovation. In particular, we present two novel interest point detectors produced through the analysis of multiple solutions that were obtained through single and multi-objective searches. Experimental results using a well-known testbed are provided to illustrate the performance of the operators and hence the effectiveness of the proposal.     

基于兴趣点定位的局部方向模式人脸识别方法

为了解决局部方向模式（LDP）在人脸特征提取过程中采用固定的平均分块方式,不能自适应突出不同样本特征的这一问题,提出一种基于兴趣点定位的改进LDP人脸特征提取方法。兴趣点所在位置特征信息丰富,其根据不同图像自动分布,可以突出不同图像的不同特点。首先定位人脸图像的加速鲁棒特征（SURF）特征点,并通过K-means聚类算法优化兴趣点的数量,确定兴趣点位置;之后以每个兴趣点作为中心建立LDP特征提取窗口,计算其4方向LDP编码,得出图像的特征向量;最后,采用支持向量机（SVM）对人脸进行识别分类。使用该改进算法分别在FERET和Yale数据库中进行实验,并与原始LDP、4方向的LDP方法（4-LDP）、融合PCA与LDP的特征提取算法（PCA-LDP）进行了比较,实验结果表明,所提出的特征提取方法在保证系统实时性的同时,可以有效提高人脸识别的准确率与稳定性。