Efficient Motion Search in Large Motion Capture Databases

This paper provides methods for identifying visually and numerically similar motions in large motion capture databases given a query of motion segment. Large human motion databases contain variants of natural motions that are valuable for animation generation and synthesis. But retrieving visually similar motions is still a difficult and time-consuming problem. We propose an efficient geometric feature based indexing strategy that represents the motions compactly through apreprocessing. This representation scales down the range of searching the database. Motions in this range are possible candidates of the final matches. For detailed comparisons between the query and the candidates, we propose an algorithm that compares the motions' curves using an efficient motion curve matching algorithm. Our methods can apply to large human motion databases and achieve high performance and accuracy compared with previous work.

The main objective of image browsers is to empower users to find a desired image with ease, speed and accuracy from a large database. In this paper we present a novel approach at creating an image browsing environment based on human perception with the aim of providing intuitive image navigation. In our approach, similarity judgments form the basic structural organization for the images in our browser. To enrich this we have developed a scalable crowd sourced method of augmenting a database with a large number of additional samples by capturing human judgments from members of a crowd. Experiments were conducted involving two databases that demonstrate the effectiveness of our method as an intuitive, fast browsing environment for large image databases.

Video cameras are widely deployed along city streets, interstate highways, traffic lights, stop signs and toll booths by entities that perform traffic monitoring and law enforcement. The videos captured by these cameras are typically compressed and stored in large databases. Performing a rapid search for a specific vehicle within a large database of compressed videos is often required and can be a time-critical life or death situation. In this paper, we propose video compression and decompression algorithms that enable fast and efficient vehicle or, more generally, event searches in large video databases. The proposed algorithm selects reference frames (i.e., I-frames) based on a vehicle having been detected at a specified position within the scene being monitored while compressing a video sequence. A search for a specific vehicle in the compressed video stream is performed across the reference frames only, which does not require decompression of the full video sequence as in traditional search algorithms. Our experimental results on videos captured in a local road show that the proposed algorithm significantly reduces the search space (thus reducing time and computational resources) in vehicle search tasks within compressed video streams, particularly those captured in light traffic volume conditions.

Processing large databases is intricate for databases involving several types of textures. In particular, for content-based image retrieval, a query has to be compared with all the samples pertaining to the database in order to identify its content/class and this is time consuming. Furthermore, modeling of a large database is a difficult task for databases involving several types of textures since accurate models for certain textures are not guaranteed to be very relevant for other types of textures and vice-versa. In order to save computational time and increase performance in processing of large texture databases, the present paper proposes structuring texture databases by using stochasticity metaclasses.

Earlier a new rough set theory for very large databases was proposed by T.Y. Lin (1996). In this paper the authors attempt to evaluate the performance of such a rough set theory for a very large database. ORACLE, a relational database management system (RDBMS), is the market leader in open system databases. Windows NT has been growing into the data server environment and is a strong contender for decision support system applications with its open and cost-effective architecture. So ORACLE running under Windows NT was used in this report. The main goal of this research is to formulate a suitable rough set theory for very large databases.

BackgroundAlthough many studies have modelled the national burdens of hospitalizations and deaths due to influenza, few studies have considered the outpatient burden. To fill this gap for the United States (US), we applied traditional statistical modelling approaches to time series derived from large medical claims databases held in the private sector.MethodsWe accessed ICD-9-coded office visit data extracted from Truven Health Analytics’ MarketScan Commercial database covering about one third of the US population <65 years during 2001–2009, and Medicare Supplemental data covering about one fifth of US seniors 65+ during 2006–2009. We extracted weekly time series of visits due to respiratory diagnoses, otitis media (OM), and urinary tract infections (UTI), a “negative control”. We used multiple linear regression modelling to estimate age-specific influenza-related excess in office visits.ResultsIn the <65 year age group, in the 8 pre-pandemic seasons studied and for the broadest defined respiratory outcome, the model attributed an average of ~14.5 M (Standard deviation [SD] across seasons 3.9 million) office visits to influenza (rate of 5,581/100,000 population). Of these, ~80 % of visits occurred in the 5–17 and 18–49 age group. In school children aged 5–17 year olds and adult 18–64 year age groups the majority of visits were due to influenza B, while A/H3N2 explained most visits in children <5 year olds. The model further attributed ~2.2 M OM visits (SD across seasons 790,000) annually to influenza, of which 86 % of these occurred in children <18 years; this indicates that 6.4 % of all infants <2 years and 4.9 % of all toddlers aged 2–4 years in the US have an influenza-attributable outpatient visit with an OM diagnosis. In seniors 65 years and older, our model attributed ~0.7 M (SD across seasons 351,000) respiratory visits to influenza (rate of 1,887/100,000 population). The model identified no significant excess UTI (negative control) visits in most seasons.ConclusionsThis is to our knowledge a first study of the outpatient burden of influenza in the US in a large database. The model estimated that 10 % of all children <18 years and 4 % of the entire population <65 years seek outpatient care for respiratory illness attributable to influenza annually.Trial registrationClinicalTrial.gov, NCT02019732.

Artificial intelligence: Machine learning approach for screening large database and drug discovery

Editors’ introduction Renouf’s contribution, like those of Scott and Berber Sardinha, uses Corpus Linguistics (CL) techniques, analysing large amounts of text by computer. The focus in her paper is on the identification of signals of semantic relations. Thus the frame “or, more exactly” might signal a general-particular relationship in “we stopped by the side of a lake, or, more exactly a loch”. Lake is more general (a superordinate) than loch (a hyponym of lake). Renouf’s endeavour was to trace signals like “more exactly” in large text databases, looking at and teasing out the meaning relations which crop up. Her work thus complements Jordan’s analysis of Basis-Assessment in this volume, using quite different methods. At the same time, she attempts to find out how good a match may be made between lexical signal and meaning — it is quite possible in language that one form might carry numerous functions (and conversely that the same meaning relation can be realised by numerous forms). This implies a need to pin down the meanings identified. The context underlying this work is that of the influential work of Cruse (1986), which takes a non-CL view of meaning, heavily dependent on logic, and the notion of contextual normality by which Cruse means the test a near-native speaker or native speaker may make, as to whether a given string like “notable events such as a solar eclipse” seems normal. This is by no means a straightforward decision. Thus in (1) notable events such as meeting the President (2) notable events such as drinking tea the degree of contextual normality would vary in rather unpredictable ways, depending on whether or not one regularly drank tea or worked in the President’s office. The approach Renouf takes is to examine large numbers of texts using a computer, to see instead what forms are actually attested. In so doing, she finds that Cruse’s neat and logical patterns do not seem to be as neatly reflected in the evidence of large numbers of examples from newspaper text. These two methods are not really a simple matter of pre-CL and post-CL, nor are they alternatives, in our view. For a start, the notion of “attested” examples in Linguistics is not new at all, and much early work before computers went into collecting slips of paper with heard or read examples of words. Perhaps the best known example is the many thousands of slips collected by innumerable contributors, for the construction of the Oxford English Dictionary. Second, we would argue that to use CL techniques does not constitute a new Linguistics, any more than using a spade constitutes “Spade Gardening”. It is merely a matter of accessing resources. Third, it is not possible to use CL techniques without recourse to one’s intuitions, e.g. as to what is contextually normal. A CL method is only able to identify positive hits: when one finds numerous examples of a given string of words, one may conclude that it must be contextually normal. If one finds one only, one does not know whether it is a joke or nonce construction or is just rather unusual; and if the string is not found even in a large database, that in itself does not guarantee that it would be contextually abnormal, since new contextually normal strings can be created at any time. Renouf’s paper thus shows that lexical semantics needs insights from logic and intuitions of contextual normality, together with CL methods enabling access to large numbers of examples.

Mining association rules in large databases is one of the most interesting data mining techniques in database communities. The number of association rules in a large database highly depends on rule’s support and confidence, and there might exist hundreds of rules in a very large database. The presentation of large number of rules in a very nice and noticeable way becomes highly challenging. In recent years researchers have developed several tools to visualize association rules. However, a large number of the tools cannot handle more than dozens of association rules. Furthermore, none of them can effectively manage association rules with multiple antecedents. Till now a uniform descriptive presentation technique has not been set up yet. We studied existing descriptive techniques in the context of visualization and introduced a graph-based technique as Unified Descriptive Language for Association Rules. The proposed technique can be used to extract the discovered rules of a very large database very conveniently and efficiently.

The purpose of this article was to discuss common issues associated with large databases and present possible solutions to improve the quality and usefulness of large database research. The volume of electronic healthcare-related data is growing exponentially. Some of these data are being stored in registries and administrative databases. These data repositories are increasingly common and can serve as sources of nurse-driven research and quality improvement activities. Although these large databases have a wealth of useful information, they have limitations that may bias results. These include missing data and cases, data accuracy and validity, and the statistical effect of large samples. Researchers using large databases to address quality, safety, clinical, or systems issues have a variety of available techniques to deal with data issues. Proper data cleaning activities such as screening, visualization, and outlier/inlier identification are essential for addressing inaccurate values within large data sets. Common methods for addressing missing data include case analyses and various imputation techniques. Statistical approaches such as risk reductions and effect size are also useful when working with large sample sizes. Registries and administrative databases provide healthcare researchers with increasing opportunities to address a wide variety of important practice and patient care questions. Healthcare researchers are encouraged to explore large data sets as they look for ways to improve patient safety and quality care, develop evidence-based practice guidelines, and fulfill regulatory and accreditation requirements.

BackgroundSimilaritysearch in chemical structure databases is an important problem with many applications in chemical genomics, drug design, and efficient chemical probe screening among others. It is widely believed that structure based methods provide an efficient way to do the query. Recently various graph kernel functions have been designed to capture the intrinsic similarity of graphs. Though successful in constructing accurate predictive and classification models, graph kernel functions can not be applied to large chemical compound database due to the high computational complexity and the difficulties in indexing similarity search for large databases. ResultsTo bridge graph kernel function and similarity search in chemical databases, we applied a novel kernel-based similarity measurement, developed in our team, to measure similarity of graph represented chemicals. In our method, we utilize a hash table to support new graph kernel function definition, efficient storage and fast search. We have applied our method, named G-hash, to large chemical databases. Our results show that the G-hash method achieves state-of-the-art performance for k-nearest neighbor (k-NN) classification. Moreover, the similarity measurement and the index structure is scalable to large chemical databases with smaller indexing size, and faster query processing time as compared to state-of-the-art indexing methods such as Daylight fingerprints, C-tree and GraphGrep.ConclusionsEfficient similarity query processing method for large chemical databases is challenging since we need to balance running time efficiency and similarity search accuracy. Our previous similarity search method, G-hash, provides a new way to perform similarity search in chemical databases. Experimental study validates the utility of G-hash in chemical databases.

A method is proposed, and shown to be able to find active compounds in a Large Database by iterative selection/screening cycles. The method is derived from genetic algorithms. Starting from an initial small parent population the next compounds to screen are selected by similarity search based on the structural features of two high rating parents. The performance of the method is demonstrated using 20000 compounds with biological data from the NCI1 database.

Mining of frequent patterns has been studied popularly in data mining area. However, very little work has been done on the problem of updating mined patterns upon threshold changes, in spite of its practical benefits. When users interactively mine frequent patterns, one difficulty is how to select an appropriate minimum support threshold. So, it is often the case that they have to continuously tune the threshold. A direct way is to re-execute the mining procedure many times with varied thresholds, which is nontrivial in large database. In this paper, an efficient Extension and Re-mining algorithm is proposed for update of previously discovered frequent patterns upon threshold changes. The algorithm proposed in this paper has been implemented and its performance is compared with re-running FP-growth algorithm under different thresholds. The study shows that our algorithm is significantly faster than the latter, especially when mining long frequent patterns in large databases.

This paper presents a new approach to personal identification using palmprints. To tackle the key issues such as feature extraction, representation, indexing, similarity measurement and fast search for the best match, we propose a hierarchical multi-feature coding scheme to facilitate coarse-to-fine matching for efficient and effective palmprint verification and identification in a large database. In contrast to the existing systems that employ a fixed mechanism for feature extraction and similarity measurement, we extract multiple features and adopt different matching criteria at different levels to achieve high performance by coarse-to-fine guided search. Our experimental results demonstrate the feasibility and effectiveness of the proposed method.

Data mining has as goal to extract knowledge from large databases. To extract this knowledge, a database may be considered as a large search space, and a mining algorithm as a search strategy. In general, a search space consists of an enormous number of elements, which make it is infeasible to search exhaustively. As a search strategy, genetic algorithms have been applied successfully in many fields. In this paper, we present a genetic algorithm-based approach for mining classification rules from large database. For emphasizing on predictive accuracy, comprehensibility and interestingness of the rules and simplifying the implementation of a genetic algorithm, we discuss detail the design of encoding, genetic operator and fitness function of genetic algorithm for this task. Experimental result shows that genetic algorithm proposed in this paper is suitable for classification rule mining and those rules discovered by the algorithm have higher classification performance to unknown data.