1622922
ClassMiner: mining medical video for scalable skimming and summarization
1. SYSTEM TECHNICAL DESCRIPTION The ClassMiner system demonstrates a fully implemented tool for scalable video skimming and summarization. The key technology in the system is the integrated medical video content structure and events mining process, which was presented in a paper at the SIGMOD workshop on Data Mining and Knowledge Discovery [1]. As the system architecture in Fig. 1 indicates, we first apply a general video shot segmentation and key-frame selection scheme to parse the video stream into physical units. Then, the video group detection, scene detection and clustering strategies are executed to mine the video content structure. Various visual and audio feature processing techniques are utilized to detect some semantic cues, such as slides, face and speaker changes, etc. within the video, and these detection results are joined together to mine three types of events (presentation, dialog, clinical operation) from the detected video scenes. Finally, a scalable video skimming and summarization tool is constructed based on the mined video content structure and event information to help the user visualize and access video content.
15136742
Mining Complex Patterns across Sequences with Gap Requirements
The recurring appearance of sequential patterns, when confined by the predefined gap requirements, often implies strong temporal correlations or trends among pattern elements. In this paper, we study the problem of mining a set of gap constrained sequential patterns across multiple sequences. Given a set of sequences S1, S2,., SK constituting a single hypersequence S, we aim to find recurring patterns in S, say P, which may cross multiple sequences with all their matching characters in S bounded by the user specified gap constraints. Because of the combinatorial candidate explosion, traditional Apriori-based algorithms are computationally infeasible. Our research proposes a new mechanism to ensure pattern growing and pruning. When combining the pruning technique with our Gap Constrained Search (GCS) and map-based support prediction approaches, our method achieves a speed about 40 times faster than its other peers.
11232317
Cost-constrained data acquisition for intelligent data preparation
Real-world data is noisy and can often suffer from corruptions or incomplete values that may impact the models created from the data. To build accurate predictive models, data acquisition is usually adopted to prepare the data and complete missing values. However, due to the significant cost of doing so and the inherent correlations in the data set, acquiring correct information for all instances is prohibitive and unnecessary. An interesting and important problem that arises here is to select what kinds of instances to complete so the model built from the processed data can receive the "maximum" performance improvement. This problem is complicated by the reality that the costs associated with the attributes are different, and fixing the missing values of some attributes is inherently more expensive than others. Therefore, the problem becomes that given a fixed budget, what kinds of instances should be selected for preparation, so that the learner built from the processed data set can maximize its performance? In this paper, we propose a solution for this problem, and the essential idea is to combine attribute costs and the relevance of each attribute to the target concept, so that the data acquisition can pay more attention to those attributes that are cheap in price but informative for classification. To this end, we will first introduce a unique economical factor (EF) that seamlessly integrates the cost and the importance (in terms of classification) of each attribute. Then, we will propose a cost-constrained data acquisition model, where active learning, missing value prediction, and impact-sensitive instance ranking are combined for effective data acquisition. Experimental results and comparative studies from real-world data sets demonstrate the effectiveness of our method.
3813357
Video data mining: semantic indexing and event detection from the association perspective
Advances in the media and entertainment industries, including streaming audio and digital TV, present new challenges for managing and accessing large audio-visual collections. Current content management systems support retrieval using low-level features, such as motion, color, and texture. However, low-level features often have little meaning for naive users, who much prefer to identify content using high-level semantics or concepts. This creates a gap between systems and their users that must be bridged for these systems to be used effectively. To this end, in this paper, we first present a knowledge-based video indexing and content management framework for domain specific videos (using basketball video as an example). We will provide a solution to explore video knowledge by mining associations from video data. The explicit definitions and evaluation measures (e.g., temporal support and confidence) for video associations are proposed by integrating the distinct feature of video data. Our approach uses video processing techniques to find visual and audio cues (e.g., court field, camera motion activities, and applause), introduces multilevel sequential association mining to explore associations among the audio and visual cues, classifies the associations by assigning each of them with a class label, and uses their appearances in the video to construct video indices. Our experimental results demonstrate the performance of the proposed approach.
5621999
Accelerated Kernel Feature Analysis
A fast algorithm, Accelerated Kernel Feature Analysis (AKFA), that discovers salient features evidenced in a sample of n unclassified patterns, is presented. Like earlier kernel-based feature selection algorithms, AKFA implicitly embeds each pattern into a Hilbert space, H, induced by a Mercer kernel. An \ell-dimensional linear subspace of H is iteratively constructed by maximizing a variance condition for the nonlinearly transformed sample. This linear subspace can then be used to define more efficient data representations and pattern classifiers. AKFA requires O(\elln2) operations, as compared to 0(n^3) for Sch¨olkof, Smola, and M¨uller’s Kernel Principal Component Analysis (KPCA), and O(\ell^2 n^2) for Smola, Mangasarian, and Sch¨olkopf’s Sparse Kernel Feature Analysis (SKFA). Numerical experiments show that AKFA can generate more concise feature representations than both KPCA and SKFA, and demonstrate that AKFA obtains similar classification performance as KPCA for a face recognition problem.
14899560
Dealing with Predictive-but-Unpredictable Attributes in Noisy Data Sources
Attribute noise can affect classification learning. Previous work in handling attribute noise has focused on those predictable attributes that can be predicted by the class and other attributes. However, attributes can often be predictive but unpredictable. Being predictive, they are essential to classification learning and it is important to handle their noise. Being unpredictable, they require strategies different from those of predictable attributes. This paper presents a study on identifying, cleansing and measuring noise for predictive-but-unpredictable attributes. New strategies are accordingly proposed. Both theoretical analysis and empirical evidence suggest that these strategies are more effective and more efficient than previous alternatives.
3096549
Kernel conditional random fields: representation and clique selection
Kernel conditional random fields (KCRFs) are introduced as a framework for discriminative modeling of graph-structured data. A representer theorem for conditional graphical models is given which shows how kernel conditional random fields arise from risk minimization procedures defined using Mercer kernels on labeled graphs. A procedure for greedily selecting cliques in the dual representation is then proposed, which allows sparse representations. By incorporating kernels and implicit feature spaces into conditional graphical models, the framework enables semi-supervised learning algorithms for structured data through the use of graph kernels. The framework and clique selection methods are demonstrated in synthetic data experiments, and are also applied to the problem of protein secondary structure prediction.
31029747
Design Tools for Application Specific Embedded Processors
A variety of factors make it increasingly difficult and expensive to design and manufacture traditional Application Specific Integrated Circuits (ASICs). Consequently, programmable alternatives are more attractive than ever. The flexibility provided by programmability comes with a performance and power overhead. This can be significantly mitigated by using application specific platforms, also referred to as Application Specific Embedded Processors, or Application Specific Instruction Set Processors (ASIPs).ASIPs and the embedded software applications running on them, require specialized design tools - both during architectural evaluation to provide feedback on the suitability of the architecture for the application; as well as during system implementation to ensure efficient mapping and validation of design constraints. These functions result in requirements different from those of traditional software development environments. The first requirement is retargetability, especially during the early architectural evaluation stage where a rapid examination of design alternatives is essential. The second requirement is for additional metrics such as power consumption, real-time constraints and code size.This paper describes a set of design tools and associated methodology designed to meet the challenges posed by architectural evaluation and software synthesis. This work is part of the MESCAL (Modern Embedded Systems, Compilers, Architectures, and Languages) project.
1251013
Congestion-aware rate allocation for multipath video streaming over ad hoc wireless networks
For multipath video streaming over ad hoc wireless networks, received video quality is influenced by both the encoder performance and the delayed packet arrivals due to limited bandwidth. We propose a rate allocation scheme to optimize the expected received video quality based on simple models of encoder rate-distortion performance and network rate-congestion tradeoffs. Given the embedded bit-streams of a group of video frames and the cross traffic condition on a given set of paths, a gradient descent method is used to determine the amount of bits to be transmitted over the multiple paths for each frame. Two rate allocation strategies, layered representation and partial repetition are also investigated. Simulation results of video streaming over a 15-node wireless ad hoc network demonstrates the benefit of performing rate allocation optimization over a simple heuristic scheme. It is also shown that while layered representation is more efficient for mild delay constraints, partial repetition is more robust against packet losses.
6174679
Correlation Clustering for Crosslingual Link Detection
The crosslingual link detection problem calls for identifying news articles in multiple languages that report on the same news event. This paper presents a novel approach based on constrained clustering. We discuss a general way for constrained clustering using a recent, graph-based clustering framework called correlation clustering. We introduce a correlation clustering implementation that features linear program chunking to allow processing larger datasets. We show how to apply the correlation clustering algorithm to the crosslingual link detection problem and present experimental results that show correlation clustering improves upon the hierarchical clustering approaches commonly used in link detection, and, hierarchical clustering approaches that take constraints into account.
15307840
Fractional chromatic number and circular chromatic number for distance graphs with large clique size
Let Z be the set of all integers and M a set of positive integers. The distance graph G(Z,M) generated by M is the graph with vertex set Z and in which i and j are adjacent whenever |i − j| ∈ M . Supported in part by the National Science Foundation under grant DMS 9805945. Supported in part by the National Science Council, R. O. C., under grant NSC892115-M-110-012.