William I. Grosky

SenseWeb: An Infrastructure for Shared Sensing

Peer-produced systems can achieve what might be infeasible for stand-alone systems developed by a single entity. The SenseWebs goal is to enable these kinds of capabilities. Using SenseWeb, applications can initiate and access sensor data streams from shared sensors across the entire Internet. The SenseWeb infrastructure helps ensure optimal sensor selection for each application and efficient sharing of sensor streams among multiple applications.

Narrowing the semantic gap - improved text-based web document retrieval using visual features

We present the results of our work that seek to negotiate the gap between low-level features and high-level concepts in the domain of web document retrieval. This work concerns a technique, called the latent semantic indexing (LSI), which has been used for textual information retrieval for many years. In this environment, LSI determines clusters of co-occurring keywords so that a query which uses a particular keyword can then retrieve documents perhaps not containing this keyword, but containing other keywords from the same cluster. In this paper, we examine the use of this technique for content-based web document retrieval, using both keywords and image features to represent the documents. Two different approaches to image feature representation, namely, color histograms and color anglograms, are adopted and evaluated. Experimental results show that LSI, together with both textual and visual features, is able to extract the underlying semantic structure of web documents, thus helping to improve the retrieval performance significantly, even when querying is done using only keywords.

Information retrieval on the World Wide Web

Effective search and retrieval are enabling technologies for realizing the full potential of the Web. The authors examine relevant issues, including methods for representing document content. They also compare available search tools and suggest methods for improving retrieval effectiveness.

Multimedia information systems

Presents a gentle introduction to multimedia information systems. The author explores the nature of multimedia data model and information system architecture, and reviews the evolution of multimedia information systems. He also discusses data model design, query processing, and browsing support, and takes a look at some state-of-the-art prototype systems.<<ETX>>

Index-based object recognition in pictorial data management

We have been involved with formulating query processing strategies for an object-oriented, integrated, textual/iconic database management system. For this task, model-based representations of images by content are being used, as well as a new, efficient object recognition approach which also permits the efficients insertion and deletion of object models, called data-driven indexed hypotheses. This paper emphasizes the data management aspects of this approach: the insertion and deletion of object models, a performance/space trade-off which can be used to improve the recognition capabilities of our approach, and a secondary memory implementation of our approach

Negotiating the semantic gap: from feature maps to semantic landscapes

In this paper, we present the results of a project that seeks to transform low-level features to a higher level of meaning. This project concerns a technique, latent semantic indexing (LSI), in conjunction with normalization and term weighting, which have been used for full-text retrieval for many years. In this environment, LSI determines clusters of co-occurring keywords, sometimes, called concepts, so that a query which uses a particular keyword can then retrieve documents perhaps not containing this keyword, but containing other keywords from the same cluster. In this paper, we examine the use of this technique for content-based image retrieval, using two different approaches to image feature representation. We also study the integration of visual features and textual keywords and the results show that it can help improve the retrieval performance significantly.

Efficient continuous skyline computation

In a number of emerging streaming applications, the data values that are produced have an associated time interval for which they are valid. A useful computation over such streaming data sets is to produce a continuous and valid skyline summary. To the best of our knowledge, this problem has not been addressed before. In this paper we introduce an operator called the continuous time-interval skyline operator for evaluating this computation. We also present a new algorithm called LookOut for evaluating the continuous time-interval skyline efficiently, and empirically demonstrate the scalability of this algorithm.

A unified approach to the linear camera calibration problem

The camera calibration process relates camera system measurements (pixels) to known reference points in a three-dimensional world coordinate system. The calibration process is viewed as consisting of two independent phases: the first is removing geometrical camera distortion so that rectangular calibration grids are straightened in the image plane, and the second is using a linear affine transformation as a map between the rectified camera coordinates and the geometrically projected coordinates on the image plane of known reference points. Phase one is camera-dependent, and in some systems may be unnecessary. Phase two is concerned with a generic model that includes 12 extrinsic variables and up to five intrinsic parameters. General methods handling additional constraints on the intrinsic variables in a manner consistent with explicit satisfaction of all six constraints on the orthogonal rotation matrix are presented. The use of coplanar and noncoplanar calibration points is described. >

Image database management

Publisher Summary Contemporary database management systems are devised to give users a seamless and transparent view into the data landscape being managed. Such programs give users the illusion that their view of the data corresponds to the way that it is actually internally represented, as if they were the only users of the software. Image database management system was conceived as a way of managing images for image algorithm development test beds. Images were retrieved based on information in header files, which contained only textual information. The architecture of a standard database management system is usually divided into three different levels, corresponding to the ANSI/SPARC standard. These levels are the physical database level, the conceptual database level, and the external database level. The implementation-independent framework that is employed to describe a database at the logical and external level is called a data model. These models represent the subject database in terms of entities, entity types, attributes of entities, operations on entities and entity types, and relationships among entities and entity types. An image data model must represent the following types of information: the model base, the model-base instantiation, the instantiation-object connection, and the object information repository. The chapter discusses some examples of the existing image database management systems classifying them as first, second, and third generation database systems. It also focuses on the similarity retrieval in image database systems.

A pictorial index mechanism for model-based matching

Abstract We are currently developing unified query processing strategies for image databases. To perform this task, model-based representations of images by content are being used, as well as a hierarchical generalization of a relatively new object-recognition technique called data-driven indexed hypotheses . As the name implies, it is index-based, from which its efficiency derives. Earlier approaches to data-driven model-based object recognition techniques were not capable of handling complex image data containing overlapping, partially visible, and touching objects due to the limitations of the features used for building models. Recently, a few data-driven techniques capable of handling complex image data have been proposed. In these techniques, as in traditional databases, iconic index structures are employed to store the image and shape representation in such a way that searching for a given shape or image feature can be conducted efficiently. Some of these techniques handle the insertion and deletion of shapes and/or image representations very efficiently and with very little influence on the overall system performance. However, the main disadvantage of all previous data-driven implementations is that they are main memory based. In the present paper, we describe a secondary memory implementation of data-driven indexed hypotheses along with some performance studies we have conducted.