34366886
AutoSplit: Fast and Scalable Discovery of Hidden Variables in Stream and Multimedia Databases
For discovering hidden (latent) variables in real-world, non-gaussian data streams or an n-dimensional cloud of data points, SVD suffers from its orthogonality constraint. Our proposed method, “AutoSplit”, finds features which are mutually independent and is able to discover non-orthogonal features. Thus, (a) finds more meaningful hidden variables and features, (b) it can easily lead to clustering and segmentation, (c) it surprisingly scales linearly with the database size and (d) it can also operate in on-line, single-pass mode. We also propose “Clustering-AutoSplit”, which extends the feature discovery to multiple feature/bases sets, and leads to clean clustering. Experiments on multiple, real-world data sets show that our method meets all the properties above, outperforming the state-of-the-art SVD.
10479007
Segmenting Motion Capture Data into Distinct Behaviors
Much of the motion capture data used in animations, commercials, and video games is carefully segmented into distinct motions either at the time of capture or by hand after the capture session. As we move toward collecting more and longer motion sequences, however, automatic segmentation techniques will become important for processing the results in a reasonable time frame.We have found that straightforward, easy to implement segmentation techniques can be very effective for segmenting motion sequences into distinct behaviors. In this paper, we present three approaches for automatic segmentation. The first two approaches are online, meaning that the algorithm traverses the motion from beginning to end, creating the segmentation as it proceeds. The first assigns a cut when the intrinsic dimensionality of a local model of the motion suddenly increases. The second places a cut when the distribution of poses is observed to change. The third approach is a batch process and segments the sequence where consecutive frames belong to different elements of a Gaussian mixture model. We assess these three methods on fourteen motion sequences and compare the performance of the automatic methods to that of transitions selected manually.
6657323
"GeoPlot": spatial data mining on video libraries
Are "tornado" touchdowns related to "earthquakes"? How about to "floods", or to "hurricanes"? In Informedia [14], using a gazetteer on news video clips, we map news onto points on the globe and find correlations between sets of points. In this paper we show how to find answers to such questions, and how to look for patterns on the geo-spatial relationships of news events. The proposed tool is "GeoPlot", which is fast to compute and gives a lot of useful information which traditional text retrieval can not find.We describe our experiments on 2-year worth of video data (~ 20 Gbytes). There we found that GeoPlot can find unexpected correlations that text retrieval would never find, such as those between "earthquake" and "volcano", and "tourism" and "wine".In addition, GeoPlot provides a good visualization of a data set's characteristics. Characteristics at all scales are shown in one plot and a wealth of information is given, for example, geo-spatial clusters, characteristic scales, and intrinsic (fractal) dimensions of the events' locations.
10596767
Automatic mining of fruit fly embryo images
We present FEMine, an automatic system for image-based gene expression analysis. We perform experiments on the largest publicly available collection of Drosophila ISH (in situ hybridization) images, showing that our FEMine system achieves excellent performance in classification, clustering, and content-based image retrieval. The major innovation of FEMine is the use of automatically discovered latent spatial "themes" of gene expressions, LGEs, in the whole-embryo context, as opposed to patterns in nearly disjoint portions of an embryo proposed in previous methods.
7681917
ViVo: visual vocabulary construction for mining biomedical images
Given a large collection of medical images of several conditions and treatments, how can we succinctly describe the characteristics of each setting? For example, given a large collection of retinal images from several different experimental conditions (normal, detached, reattached, etc.), how can data mining help biologists focus on important regions in the images or on the differences between different experimental conditions? If the images were text documents, we could find the main terms and concepts for each condition by existing IR methods (e.g., tf/idf and LSI). We propose something analogous, but for the much more challenging case of an image collection: We propose to automatically develop a visual vocabulary by breaking images into n /spl times/ n tiles and deriving key tiles ("ViVos") for each image and condition. We experiment with numerous domain-independent ways of extracting features from tiles (color histograms, textures, etc.), and several ways of choosing characteristic tiles (PCA, ICA). We perform experiments on two disparate biomedical datasets. The quantitative measure of success is classification accuracy: Our "ViVos" achieve high classification accuracy (up to 83 %for a nine-class problem on feline retinal images). More importantly, qualitatively, our "ViVos" do an excellent job as "visual vocabulary terms": they have biological meaning, as corroborated by domain experts; they help spot characteristic regions of images, exactly like text vocabulary terms do for documents; and they highlight the differences between pairs of images.
2316797
MMSS: multi-modal story-oriented video summarization
We propose multi-modal story-oriented video summarization (MMSS) which, unlike previous works that use fine-tuned, domain-specific heuristics, provides a domain-independent, graph-based framework. MMSS uncovers correlation between information of different modalities which gives meaningful story-oriented news video summaries. MMSS can also be applied for video retrieval, giving performance that matches the best traditional retrieval techniques (OKAPI and LSI), with no fine-tuned heuristics such as tf/idf.
6676888
Constrained Ant Colony Optimization for Data Clustering
Processes that simulate natural phenomena have successfully been applied to a number of problems for which no simple mathematical solution is known or is practicable. Such meta-heuristic algorithms include genetic algorithms, particle swarm optimization and ant colony systems and have received increasing attention in recent years. 
 
This paper extends ant colony systems and discusses a novel data clustering process using Constrained Ant Colony Optimization (CACO). The CACO algorithm extends the Ant Colony Optimization algorithm by accommodating a quadratic distance metric, the Sum of K Nearest Neighbor Distances (SKNND) metric, constrained addition of pheromone and a shrinking range strategy to improve data clustering. We show that the CACO algorithm can resolve the problems of clusters with arbitrary shapes, clusters with outliers and bridges between clusters.
768133
Performance analysis of TCP-friendly AIMD algorithms for multimedia applications
In this paper, the performance of TCP-friendly generic AIMD (Additive Increase and Multiplicative Decrease) algorithms for Web-based playback and multirate multimedia applications is investigated. The necessary and sufficient TCP-friendly condition is derived, and the effectiveness and responsiveness of AIMD are studied. Due to practical implications, a Dynamic TCP-friendly AIMD (DTAIMD) algorithm is proposed. Extensive simulation results are given to verify the derived necessary and sufficient condition, and to demonstrate the performance of the proposed DTAIMD algorithm.
13419311
Metamodeling Architecture of Web Ontology Languages
Recent research has shown that RDF Schema, as a schema layer Semantic Web language, has a non-standard metamodeling architecture. As a result, it is difficult to understand and lacks clear semantics. This paper proposes a fixed layer metamodeling architecture for RDF Schema (RDFS(FA)) and demonstrates how the problems of RDF Schema can be solved under RDFS(FA). Based on this metamodeling architecture, a clear model-theoretic semantics of RDFS(FA) is given. Interestingly, RDFS(FA) also benefits DAML+OIL by offering a firm semantic basis and by solving the "layer mistake" problem.
14220183
Web Ontology Reasoning with Datatype Groups
When providing reasoning services for ontology languages such as DAML+OIL and OWL, it is necessary for description logics to deal with "concrete" datatypes (strings, integers, etc.) as well as "abstract" concepts and relationships. In this paper, we present a new approach, the datatype group approach, to integrating DLs with multiple datatypes. We discuss the advantages of such approach over the existing ones and show how a tableaux algorithm for the description logic SHOQ(Dn) can be modified in order to reason with datatype groups.
6653256
Domain-constrained semi-supervised mining of tracking models in sensor networks
Accurate localization of mobile objects is a major research problem in sensor networks and an important data mining application. Specifically, the localization problem is to determine the location of a client device accurately given the radio signal strength values received at the client device from multiple beacon sensors or access points. Conventional data mining and machine learning methods can be applied to solve this problem. However, all of them require large amounts of labeled training data, which can be quite expensive. In this paper, we propose a probabilistic semi supervised learning approach to reduce the calibration effort and increase the tracking accuracy. Our method is based on semi-supervised conditional random fields which can enhance the learned model from a small set of training data with abundant unlabeled data effectively. To make our method more efficient, we exploit a Generalized EM algorithm coupled with domain constraints. We validate our method through extensive experiments in a real sensor network using Crossbow MICA2 sensors. The results demonstrate the advantages of methods compared to other state-of-the-art object-tracking algorithms.