Nonlinear dimensionality reduction for efficient and effective audio similarity searching

In this paper, we address the issue of nonlinear dimensionality reduction to efficiently index spectral audio similarity measures. We propose the embedding of the spectral similarity space to a low-dimensional Euclidean space. This guarantees the triangular inequality and allows the adoption of several indexing schemes. We enlighten the advantages of the proposed indexable method against recently proposed spectral similarity measures that are also indexable. Moreover, our method compares favorably to linear dimensionality reduction methods, like multidimensional scaling (MDS). The proposed method significantly reduces the computation time during the construction process compared to any audio measure and, simultaneously, minimizes the searching cost for similar songs. To the best of our knowledge, the important issue of audio similarity measures’ scalability is addressed for the first time.     

Effective and efficient similarity search in scientific workflow repositories

Scientific workflows have become a valuable tool for large-scale data processing and analysis. This has led to the creation of specialized online repositories to facilitate workflow sharing and reuse. Over time, these repositories have grown to sizes that call for advanced methods to support workflow discovery, in particular for similarity search. Effective similarity search requires both high quality algorithms for the comparison of scientific workflows and efficient strategies for indexing, searching, and ranking of search results. Yet, the graph structure of scientific workflows poses severe challenges to each of these steps. Here, we present a complete system for effective and efficient similarity search in scientific workflow repositories, based on the Layer Decomposition approach to scientific workflow comparison. Layer Decomposition specifically accounts for the directed dataflow underlying scientific workflows and, compared to other state-of-the-art methods, delivers best results for similarity search at comparably low runtimes. Stacking Layer Decomposition with even faster, structure-agnostic approaches allows us to use proven, off-the-shelf tools for workflow indexing to further reduce runtimes and scale similarity search to sizes of current repositories.     

Indexing high-dimensional data for efficient in-memory similarity search

In main memory systems, the L2 cache typically employs cache line sizes of 32-128 bytes. These values are relatively small compared to high-dimensional data, e.g., >32D. The consequence is that existing techniques (on low-dimensional data) that minimize cache misses are no longer effective. We present a novel index structure, called /spl Delta/-tree, to speed up the high-dimensional query in main memory environment. The /spl Delta/-tree is a multilevel structure where each level represents the data space at different dimensionalities: the number of dimensions increases toward the leaf level. The remaining dimensions are obtained using principal component analysis. Each level of the tree serves to prune the search space more efficiently as the lower dimensions can reduce the distance computation and better exploit the small cache line size. Additionally, the top-down clustering scheme can capture the feature of the data set and, hence, reduces the search space. We also propose an extension, called /spl Delta//sup +/-tree, that globally clusters the data space and then partitions clusters into small regions. The /spl Delta//sup +/-tree can further reduce the computational cost and cache misses. We conducted extensive experiments to evaluate the proposed structures against existing techniques on different kinds of data sets. Our results show that the /spl Delta//sup +/-tree is superior in most cases.     

基于运动捕获数据的虚拟人动画研究

随着三维游戏等行业对计算机动画制作需求的增加,在三维动画制作软件中人工调整虚拟人动作的工作方式已经不再适合现在的计算机动画制作。运动捕获技术是直接记录物体的运动数据并将其用于生成计算机动画,具有高效率、所生成的动画真实感强等优点,因而获得了广泛应用。提出了一种利用运动捕获数据来生成动画的方法,基于运动捕获得到的数据建立并驱动三维骨架模型,从而产生骨架的运动,形成动画。该方法可以充分利用现有的大量运动捕获数据,因此具有较大的应用前景。     

基于PGA的人体运动捕获数据分割方法

由于人体运动捕获数据的固有非线性,线性方法并不总是能够有效地找到运动捕获数据的内在维度,针对这种情况,提出了基于主测地线分析（PGA）和概率主测地线分析（PPGA）的自动分割方法。这两种方法都将人体运动视为一个有序的姿势序列,并在姿势序列有局部变化处对运动进行分割。基于PGA的分割方法是在运动局部模型的内在维度突然增长处分配一个分割点,基于PPGA的分割方法是在姿势分布发生改变时放置分割点。实验结果表明,该方法都能实现自动分割,且具有较好的分割结果。     

Review of techniques for motion capture data processing

In order to high reality and efficiency,the technique of motion capture (MoCap) has been widely used in the field of computer animation.With the development of motion capture,a large amount of motion capture databases are available and this is significant for the reuse of motion data.But due to the high degree of freedoms and high capture frequency,the dimension of the motion capture data is usually very high and this will lead to a low efficiency in data processing.So how to process the high dimension data and design an efficient and effective retrieval approach has become a challenge which we can’t ignore.In this paper,first we lay out some problems about the key techniques in motion capture data processing.Then the existing approaches are analyzed and summarized.At last,some future work is proposed.     

Efficient motion data indexing and retrieval with local similarity measure of motion strings

Widely used in data-driven computer animation, motion capture data exhibits its complexity both spatially and temporally. The indexing and retrieval of motion data is a hard task that is not totally solved. In this paper, we present an efficient motion data indexing and retrieval method based on self-organizing map and Smith–Waterman string similarity metric. Existing motion clips are first used to train a self-organizing map and then indexed by the nodes of the map to get the motion strings. The Smith–Waterman algorithm, a local similarity measure method for string comparison, is used in clustering the motion strings. Then the motion motif of each cluster is extracted for the retrieval of example-based query. As an unsupervised learning approach, our method can cluster motion clips automatically without needing to know their motion types. Experiment results on a dataset of various kinds of motion show that the proposed method not only clusters the motion data accurately but also retrieves appropriate motion data efficiently.     

Segmentation and recognition of motion capture data stream by classification

Three dimensional human motions recorded by motion capture and hand gestures recorded by using data gloves generate variable-length data streams. These data streams usually have dozens of attributes, and have different variations for similar motions. To segment and recognize motion streams, a classification-based approach is proposed in this paper. Classification feature vectors are extracted by utilizing singular value decompositions (SVD) of motion data. The extracted feature vectors capture the dominating geometric structures of motion data as revealed by SVD. Multi-class support vector machine (SVM) classifiers with class probability estimates are explored for classifying the feature vectors in order to segment and recognize motion streams. Experiments show that the proposed approach can find patterns in motion data streams with high accuracy.

一种新的光学运动捕捉数据处理方法*

提出一种适用于被动式光学人体运动捕捉散乱数据处理方法。该方法基于光学人体运动捕捉散乱数据的全局信息,提出基于模块分段线性模型的数据处理算法。利用模块分段线性模型归纳出不同模块的变化特征,从而确定各模块数据的匹配优先级及段内拟合函数,有效地对三维运动数据各模块进行全局性分层次预测和跟踪,并对噪声数据进行基于模块的去噪处理;对缺失运动数据提出基于分段Newton插值拟合算法,进行合理的补缺。该方法经优化后在处理过程中无须人工干预,并能满足实时性要求。     

Human motion capture data retrieval based on semantic thumbnail

We present a method for the efficient retrieval and browsing of immense amounts of realistic 3D human body motion capture data. The proposed method organizes motion capture data based on statistical K-means (SK–means), democratic decision making, unsupervised learning, and visual key frame extraction, thus achieving intuitive retrieval by browsing thumbnails of semantic key frames. We apply three steps for the efficient retrieval of motion capture data. The first is obtaining the basic type clusters by clustering motion capture data using the novel SK-means algorithm, and after which, immediately performing character matching. The second is learning the retrieval information of users during the retrieval process and updating the successful retrieval rate of each data; the search results are then ranked on the basis of successful retrieval rate by democratic decision making to improve accuracy. The last step is generating thumbnails with semantic generalization, which is conducted by using a novel key frame extraction algorithm based on visualized data analysis. The experiment demonstrates that this method can be utilised for the efficient organization and retrieval of enormous motion capture data.     

Effective and efficient crowd-assisted similarity retrieval of medical images in resource-constraint Mobile telemedicine systems

This paper presents an effective and efficient framework for Crowd-assisted Mobile Similarity Retrieval of the large-scale medical images in the resource-constraint mobile telemedicine systems (MTS), called the CMSR. The CMSR processing works as follows: when a user submits a retrieval medical image I_R, a buffer checking processing is first invoked to check if the full (or partial) retrieval results have been cached in the buffer previously. After that, a parallel image data filtering and refinement processing is conducted at a master node level. Finally, the candidate images are concurrently validated by a mCrowd system to derive an answer set that is transmitted to the retrieval node. To better facilitate the effective and efficient CMSR processing, three enabling techniques, i.e., category-based image data interleaving placement scheme, hindex-support image filtering algorithm and a kNN-based buffering scheme are developed. To improve the retrieval throughput, finally, we propose an extension of the CMSR method called mCMSR to optimize the multiple CMSRs. The experimental results show that the performances of the CMSR and the mCMSR methods are: 1) effective in improving the retrieval accuracy; 2) efficient in minimizing the response time by decreasing the network transmission cost while increasing the parallelism of I/O and CPU.

     

Techniques and data structures for efficient multimedia retrieval based on similarity

As more and more information is captured and stored in digital form, many techniques and systems have been developed for indexing and retrieval of text documents, audio, images, and video. The retrieval is normally based on similarities between extracted feature vectors of the query and stored items. Feature vectors are usually multidimensional. When the number of stored objects and/or the number of dimensions of the feature vectors are large, it will be too slow to linearly search all stored feature vectors to find those that satisfy the query criteria. Techniques and data structures are thus required to organize feature vectors and manage the search process so that objects relevant to the query can be located quickly. This paper provides a survey of these techniques and data structures.     

Searching for variable-speed motions in long sequences of motion capture data

Motion capture data digitally represent human movements by sequences of body configurations in time. Subsequence searching in long sequences of such spatio-temporal data is difficult as query-relevant motions can vary in execution speeds and styles and can occur anywhere in a very long data sequence. To deal with these problems, we employ a fast and effective similarity measure that is elastic. The property of elasticity enables matching of two overlapping but slightly misaligned subsequences with a high confidence. Based on the elasticity, the long data sequence is partitioned into overlapping segments that are organized in multiple levels. The number of levels and sizes of overlaps are optimized to generate a modest number of segments while being able to trace an arbitrary query. In a retrieval phase, a query is always represented as a single segment and fast matched against segments within a relevant level without any costly post-processing. Moreover, visiting adjacent levels makes possible subsequence searching of time-warped (i.e., faster or slower executed) queries. To efficiently search on a large scale, segment features can be binarized and segmentation levels independently indexed. We experimentally demonstrate effectiveness and efficiency of the proposed approach for subsequence searching on a real-life dataset.     

基于隐空间的运动捕获数据自动分割*

提出一种运动捕获数据自动分割方法。利用高斯过程隐变量模型对运动捕获数据进行降维,将其从高维观察空间映射到低维隐空间;在隐空间中构造运动特征函数,该函数具有结构简单、对所有关节敏感等优点,通过分析运动特征函数几何特征的变化,探测运动捕获数据的分割点,实现运动自动分割。实验结果表明,该方法具有较高的准确度和较好的普适性。     

Rate‐distortion optimized compression of motion capture data

Lossy compression of motion capture data can alleviate the problems of efficient storage and transmission by exploiting the redundancy and the superfluous precision of the data. When considering the acceptable amount of distortion, perceptual issues have to be taken into account. Current state of the art methods reduce the data rate required for high quality storage of motion capture data using various techniques. Most of them, however, do not use the common tools of general data compression, such as the method of Lagrange multipliers, and thus they obtain sub‐optimal results, making it difficult to do a fair comparison of their performance. In this paper, we present a general preprocessing step based on Lagrange multipliers, which allows to rigorously adjust the precision in each of the degrees of freedom of the input data according to the amount of influence the given degree of freedom has on the overall distortion. We then present a simple compression method based on Principal Component Analysis, which in combination with the proposed preprocessing achieves significantly better results than current state of the art methods. It allows optimization with respect to various distortion metrics, and we discuss the choice of the metric in two common but distinct scenarios, proposing a perceptually oriented comparison metric based on the relation of the problem at hand to the problem of compression of dynamic meshes.     

Multiresolution coding of motion capture data for real-time multimedia applications

Multimedia Tools and Applications - In this work, we present a novel and efficient method for coding of motion capture (MoCap) data obtained from recording of human actions. MoCap data is...     

An efficient and effective similarity measure to enable data mining of petroglyphs

Rock art is an archaeological term for human-made markings on stone, including carved markings, known as petroglyphs, and painted markings, known as pictographs. It is believed that there are millions of petroglyphs in North America alone, and the study of this valued cultural resource has implications even beyond anthropology and history. Surprisingly, although image processing, information retrieval and data mining have had a large impact on many human endeavors, they have had essentially zero impact on the study of rock art. In this work we identify the reasons for this, and introduce a novel distance measure and algorithms which allow efficient and effective data mining of large collections of rock art.     

人体运动实时捕捉设备传输数据的滤波与处理

简介人体运动实时捕捉设备的工作原理,对设备探测并传输来的数据进行特性分析,根据分析结论,提出了干扰数据识别与修复、纵向平滑滤波、首帧收敛化处理和值域限定等滤波和处理方法,并详细阐述了这些方法的综合运用。系统使用这些方法,经运行调试后,传输数据稳定,系统对Marker点的探测精度也符合系统设计的要求（≤6mm）,为实现各点坐标计算提供了必要条件。