6835552
Identifying Color in Motion in Video Sensors
Identifying or matching the surface color of a moving object in surveillance video is critical for achieving reliable object-tracking and searching. Traditional color models provide little help, since the surface of an object is usually not flat, the object’s motion can alter the surface’s orientation, and the lighting conditions can vary when the object moves. To tackle this research problem, we conduct extensive data mining on video clips collected under various lighting conditions and distances from several video-cameras. We observe how each of the eleven culture colors can drift in the color space when an object’s surface is in motion. In the color space, we then learn the drift pattern of each culture color for classifying unseen surface colors. Finally, we devise a distance function taking color drift into consideration to perform color identification and matching. Empirical studies show our approach to be very promising: achieving over 95% color-prediction accuracy.
6932749
Adaptive stream resource management using Kalman Filters
To answer user queries efficiently, a stream management system must handle continuous, high-volume, possibly noisy, and time-varying data streams. One major research area in stream management seeks to allocate resources (such as network bandwidth and memory) to query plans, either to minimize resource usage under a precision requirement, or to maximize precision of results under resource constraints. To date, many solutions have been proposed; however, most solutions are ad hoc with hard-coded heuristics to generate query plans. In contrast, we perceive stream resource management as fundamentally a filtering problem, in which the objective is to filter out as much data as possible to conserve resources, provided that the precision standards can be met. We select the Kalman Filter as a general and adaptive filtering solution for conserving resources. The Kalman Filter has the ability to adapt to various stream characteristics, sensor noise, and time variance. Furthermore, we realize a significant performance boost by switching from traditional methods of caching static data (which can soon become stale) to our method of caching dynamic procedures that can predict data reliably at the server without the clients' involvement. In this work we focus on minimization of communication overhead for both synthetic and real-world streams. Through examples and empirical studies, we demonstrate the flexibility and effectiveness of using the Kalman Filter as a solution for managing trade-offs between precision of results and resources in satisfying stream queries.
5061452
Mragyati : A System for Keyword-based Searching in Databases
The web, through many search engine sites, has popularized the keyword-based search paradigm, where a user can specify a string of keywords and expect to retrieve relevant documents, possibly ranked by their relevance to the query. Since a lot of information is stored in databases (and not as HTML documents), it is important to provide a similar search paradigm for databases, where users can query a database without knowing the database schema and database query languages such as SQL. In this paper, we propose such a database search system, which accepts a free-form query as a collection of keywords, translates it into queries on the database using the database metadata, and presents query results in a well-structured and browsable form. Th eysytem maps keywords onto the database schema and uses inter-relationships (i.e., data semantics) among the referred tables to generate meaningful query results. We also describe our prototype for database search, called Mragyati. Th eapproach proposed here is scalable, as it does not build an in-memory graph of the entire database for searching for relationships among the objects selected by the user's query.
5467372
Organizing and Searching the World Wide Web of Facts - Step One: The One-Million Fact Extraction Challenge
Due to the inherent difficulty of processing noisy text, the potential of the Web as a decentralized repository of human knowledge remains largely untapped during Web search. The access to billions of binary relations among named entities would enable new search paradigms and alternative methods for presenting the search results. A first concrete step towards building large searchable repositories of factual knowledge is to derive such knowledge automatically at large scale from textual documents. Generalized contextual extraction patterns allow for fast iterative progression towards extracting one million facts of a given type (e.g., Person-BornIn-Year) from 100 million Web documents of arbitrary quality. The extraction starts from as few as 10 seed facts, requires no additional input knowledge or annotated text, and emphasizes scale and coverage by avoiding the use of syntactic parsers, named entity recognizers, gazetteers, and similar text processing tools and resources.
16967659
Content-based adaptive compression of educational videos using phase correlation techniques
A serious bottleneck towards multimedia e-learning is the non-availability of required bandwidth to view the lecture videos at good resolution because of large size of lecture videos. Content-based compression of video data can greatly enhance the bandwidth utilization over scarce resource networks. In this paper, an educational video compression technique is presented that dynamically allocates the space according to the content importance of each video segment in the educational videos. We present a phase-correlation-based motion estimation and compensation algorithm that assists in the compression of important moving objects in an efficient manner. Temporal coherence is exploited in a two-phase manner. First, the frames with high similarity are categorized and encoded efficiently. Second, the compression ratio is adapted according to the frame content. Shots that are of high importance are stored at a higher bit rate as compared to the frames of relatively low importance. The importance and priority of the frames is computed automatically by our algorithm. Results over several hours of educational videos and comparison with the state-of-the-art compression algorithms illustrate the high compression performance of our technique.
13887159
JANUS: Speech-to-Speech Translation Using Connectionist and Non-Connectionist Techniques
We present JANUS, a speech-to-speech translation system that utilizes diverse processing strategies, including connectionist learning, traditional AI knowledge representation approaches, dynamic programming, and stochastic techniques. JANUS translates continuously spoken English and German into German, English, and Japanese. JANUS currently achieves 87% translation fidelity from English speech and 97% from German speech. We present the JANUS system along with comparative evaluations of its interchangeable processing components, with special emphasis on the connectionist modules.
39123560
High Quality Compression of Educational Videos Using Content-Adaptive Framework
With multimedia E-learning becoming popular, better compression algorithms are required that are space efficient but maintain the quality of video data, particularly slide and blackboard text. In this paper, an educational video compression technique is presented that dynamically allocates the space according to the content importance of each video segment in the educational videos. We present a phase correlation based motion estimation and compensation algorithm to encode important moving objects in efficient manner. Temporal coherence is exploited in a two phase manner. First, the frames with high similarity are categorized and encoded efficiently. Secondly, the compression ratio is adapted according to the frame content. The algorithm is compared with the state-of-the-art standards such as H.261, MPEG-4, etc. on large database. The comparison shows that for similar bit rates, the video quality for our algorithm is significantly better than the other methods.
8533646
Dimensionality reduction using genetic algorithms
Pattern recognition generally requires that objects be described in terms of a set of measurable features. The selection and quality of the features representing each pattern affect the success of subsequent classification. Feature extraction is the process of deriving new features from original features to reduce the cost of feature measurement, increase classifier efficiency, and allow higher accuracy. Many feature extraction techniques involve linear transformations of the original pattern vectors to new vectors of lower dimensionality. While this is useful for data visualization and classification efficiency, it does not necessarily reduce the number of features to be measured since each new feature may be a linear combination of all of the features in the original pattern vector. Here, we present a new approach to feature extraction in which feature selection and extraction and classifier training are performed simultaneously using a genetic algorithm. The genetic algorithm optimizes a feature weight vector used to scale the individual features in the original pattern vectors. A masking vector is also employed for simultaneous selection of a feature subset. We employ this technique in combination with the k nearest neighbor classification rule, and compare the results with classical feature selection and extraction techniques, including sequential floating forward feature selection, and linear discriminant analysis. We also present results for the identification of favorable water-binding sites on protein surfaces.