17019459
Semantic video classification by integrating flexible mixture model with adaptive EM algorithm
Digital video now plays an important role in medical education and healthcare, but our ability to automatic video indexing at semantic level is currently primitive. In this paper, we propose a novel framework to enable more effective semantic video classification and indexing in a specific surgery education video domain. Specifically, this framework includes: (a) A novel semantic-sensitive video content characterization and representation framework by using principal video shots and their perceptual multimodal features. (b) A novel semantic medical concept interpretation technique by using flexible mixture model. (c) A semantic video classifier by using an adaptive Expectation-Maximization (EM) algorithm for automatic parameter estimation and model selection (i.e., selecting the optimal number of mixture Gaussian components). Since more effective video content characterization framework has been integrated with an adaptive EM algorithm for video classification, our semantic video classifier has improved the classification accuracy significantly. For skin classification, its accuracy is close to 95.5%. For semantic surgical video classification, it achieves overall ≈ 84.6% accuracy.
8765334
Privacy preserving regression modelling via distributed computation
Reluctance of data owners to share their possibly confidential or proprietary data with others who own related databases is a serious impediment to conducting a mutually beneficial data mining analysis. We address the case of vertically partitioned data -- multiple data owners/agencies each possess a few attributes of every data record. We focus on the case of the agencies wanting to conduct a linear regression analysis with complete records without disclosing values of their own attributes. This paper describes an algorithm that enables such agencies to compute the exact regression coefficients of the global regression equation and also perform some basic goodness-of-fit diagnostics while protecting the confidentiality of their data. In more general settings beyond the privacy scenario, this algorithm can also be viewed as method for the distributed computation for regression analyses.
8754175
Summarizing level-two topological relations in large spatial datasets
Summarizing topological relations is fundamental to many spatial applications including spatial query optimization. In this article, we present several novel techniques to effectively construct cell density based spatial histograms for range (window) summarizations restricted to the four most important level-two topological relations: contains, contained, overlap, and disjoint. We first present a novel framework to construct a multiscale Euler histogram in 2D space with the guarantee of the exact summarization results for aligned windows in constant time. To minimize the storage space in such a multiscale Euler histogram, an approximate algorithm with the approximate ratio 19/12 is presented, while the problem is shown NP-hard generally. To conform to a limited storage space where a multiscale histogram may be allowed to have only k Euler histograms, an effective algorithm is presented to construct multiscale histograms to achieve high accuracy in approximately summarizing aligned windows. Then, we present a new approximate algorithm to query an Euler histogram that cannot guarantee the exact answers; it runs in constant time. We also investigate the problem of nonaligned windows and the problem of effectively partitioning the data space to support nonaligned window queries. Finally, we extend our techniques to 3D space. Our extensive experiments against both synthetic and real world datasets demonstrate that the approximate multiscale histogram techniques may improve the accuracy of the existing techniques by several orders of magnitude while retaining the cost efficiency, and the exact multiscale histogram technique requires only a storage space linearly proportional to the number of cells for many popular real datasets.
1977537
Active layout engine: Algorithms and applications in variable data printing
Variable Data Printing (VDP) refers to the process of generating and printing dynamic or personalized contents. A core technology required by highly customized VDP applications is the automatic document layout design engine, whose task is to adjust the original design or generate a new layout to present variable contents. This paper presents a novel document layout design engine, called Active Layout Engine (ALE). 'Active' reflects several unique features of the engine: first, through linear text block modeling and two-pass constraint solving algorithm, it supports a rich set of layout operations, such as simultaneous optimization of text block width and height, integrated image cropping, and non-rectangular text wrapping. Second, it does not rely on a particular layout description language and thus can actively pursue emerging formats and standards. This paper describes the various technical aspects of ALE: linearization of the text block modeling, two-pass constraint solving algorithm, format-neutral Active Layout Template (ALT), system optimization, and typical VDP applications around the core engine.
21113165
Graphical representations of electronic search patterns
Increasing numbers of researchers and developers are collecting data that trace the processes of humancomputer interactions. Although a variety of methods are used to collect such data, all researchers share common problems in representing and analyzing continuous streams of data. A set of methods and tools for graphically representing the search patterns of users interacting with a variety of databases and hypertexts is under development as part of ongoing research in electronic information seeking. The methodology described in this article involves defining state spaces of user options within a system, capturing keystrokes or mouse moves as raw data, coding these data into the state spaces, and analyzing user moves through the state space as evidence of goal-directed informationseeking strategies and patterns. A tool for displaying user moves through a state space has been developed and is described. The tool graphically represents data in a variety of formats, allowing human pattern recognition capabilities to be applied for data analysis. Graphs can be represented for individual searches or for multiple searches done by either a single searcher or by several searchers working on the same search task.
17501980
Term Graph Model for Text Classification
Most existing text classification methods (and text mining methods at large) are based on representing the documents using the traditional vector space model. We argue that important information, such as the relationship among words, is lost. We propose a term graph model to represent not only the content of a document but also the relationship among the keywords. We demonstrate that the new model enables us to define new similarity functions, such as considering rank correlation based on PageRank-style algorithms, for the classification purpose. Our preliminary results show promising results of our new model.
16311352
DIAL 2004 working group report on acquisition quality control
This report summarizes the discussions of the Working Group on Acquisition Quality at the International Workshop on Document Image Analysis for Libraries, Palo Alto, CA, 23-24 January 2004. Acquisition of the image is one of the most time intensive components of forming a digital library, and the quality of the acquisition will affect all later stages of the digital library project. The current state of the art in acquisition is analyzed. Problems and suggested improvements for image acquisition and storage formats and the special problems associated with acquisition from microfilm follows. A list of general suggestions was developed which was complemented by a wish list of things the Working Group would like to see followed in acquisition discussions in the future