Recent explosion in the use of non-text, multimedia data such as audio, video, images, and graphics necessitates the development and use of multimedia databases. Efficient schemes for indexing and searching in multimedia databases are essential for fast and sophisticated data retrieval. The highly complex nature of audio/visual query processing, and the vast amounts of data in multimedia databases, require the use of parallel algorithms for indexing and searching to achieve the desired efficiency in data retrievals. Indeed, high-performance parallel processing systems are beginning to be used as servers for multimedia databases, alongside workstations and PCs with multiple processors, as clients. While some work has been done on image and video databases, little attention has been paid to audio databases. In this paper we design and analyze two parallel algorithms for searching for audio data in audio/multimedia databases, using a transform-based indexing scheme. 1 Introduction The d...

Many visual databases (including image and video databases) are being designed in various locations. The integration of such databases enables users to access data across the world in a transparent manner. In this article, we present a system framework, termed NetView, which supports global content-based query access to various visual databases over the Internet. An integrated metaserver is designed to facilitate such access. The metaserver contains three main components: the metadatabase, the metasearch agent, and the query manager. The metadatabase is organized to include the metadata about individual visual databases which reflect the semantics of each visual database. The query manager extracts heterogeneous features such as text, texture, and color in the query for suitable matching of the metadata. The metasearch agent derives a list of relevant database sites to the given query by matching their feature content with the metadata. Such a capability can significantly reduce the a...

The representation, matching and analysis of objects of interest are of prime importance in shape-based retrieval systems. Considering that these systems involve analysis of various complex shapes, an accurate representation of free form shape is required. A simple and accurate shape representation procedure would ensure meaningful results from the shape-based retrieval systems. Motivated by this factor, this paper presents a free form shape representation technique using Non-Uniform Rational B-Spline (NURBS) modelling. The free form shapes are modelled using control points and weights. NURBS posses attractive properties such as spatial uniqueness, bounded and continuous, local shape controllability and shape invariance under transformation. Furthermore, NURBS based shape descriptor allows accurate reconstruction of the shape boundary from the NURBS features used to describe it. This paper presents the details of deriving a set of NURBS features using the boundary of the object. The accuracy and data reduction properties using NURBS are examined by carrying out an experiment on two sets of images: geometric and free form. Accuracy of the representation is evaluated by using centroidradii error function, which computes the cumulative distance between the intersection points by radii lines on the boundary of the original image and the reconstructed image. The data reduction property is shown by the ratio computation between the number of control points and the boundary points. The overall experiment results show that NURBS is an accurate shape descriptor and a potential candidate for use in shape-based image retrieval systems. Keywords: Shape Representation, NURBS, Shape-Based Image Retrieval 1.

..................................................................................................................................................................................... 3 1 INTRODUCTION .................................................................................................................................................................. 3 2 MULTIMEDIA DATA RETRIEVAL ................................................................................................................................... 6 2.1 QUERYING APPROACHES ....................................................................................................................................................... 6 2.2 SIMILARITY SEARCH ............................................................................................................................................................. 6 2.3 REGULAR WEIGHTED COMPARISON......................................................................................

The huge data sizes of images, the enormous number of images in a typical image database, coupled with the inexact nature, and subjective interpretations of images have called for content-based retrievals, which place enormous demands on computation. Network of workstations (NOWs) are a cost-effective way of providing the much needed computational power in such applications. This paper presents a distributed scheme for image retrievals in a NOW system, by using an initial classification of images in the database. It uses a heuristic for determining the centroids and thresholds for the image classes, which are used in the search process. The retrieval results using the proposed scheme on a NOW system are presented. Keywords: Image databases, image classes, content-based retrievals, distributed algorithm, network-of-workstations. 1 Introduction There has recently been phenomenal increases in the use of images, along with text and other multimedia data such as audio and video, in a va...

Researchers are currently more interested in searching for fragments that are similar to a query, than a total data item that is similar to a query; the search interest is for "contains", not "is". This paper presents an ) (logn O algorithm, called the "generalized virtual node (GVN)" algorithm; the GVN algorithm is a search algorithm for data fragments that have similar contents to that of a query. An example of the use of the GVN algorithm is in the search of a database of audio recordings for a few measures of a melody. Each audio clip is transformed into characteristic features and these features are stored in a hierarchical multidimensional structure, called a "k-tree." The k-tree is exploited to build a unified retrieval model for any types of multimedia data. The experimental results of this "in-clip" search algorithm on an audio database demonstrate a search quality is qualitatively and quantitatively acceptable, with a retrieval time is faster than other algorithms, such as b...

In this paper, we propose a model for the fast retrieval image data using multiple features based on multiresolution processing. We demonstrate our performance results by searching an image database of approximately 30,000 24-bit color photographic images. We use identical structures to build the index keys from the two features using histogram generation and wavelet transform. The experiments show that the results obtained with the quad-tree structure for the two features are more accurate than using just one feature; however, the retrieval time almost doubles. The retrieval time can be improved by applying our weighted cascading comparison scheme, which allows the weighting of the features to control a pipeline. We evaluate our multiplefeature similarity-matching algorithm by comparing its accuracy with a regular-matching algorithm. The results also show that the retrieval time of our weighted cascading scheme is faster than the regular matching algorithm with an acceptable accuracy....

In this paper, we propose a static data partitioning scheme for content-based multimedia data retrieval using a heterogeneous cluster system. Multimedia data is represented by unified k-tree structure of kdimensional (k-d) signals proposed in [6]. Each dimension of k-d data is separated into small blocks and then formed into a hierarchical multidimensional tree structure, called a k-tree. The parallel version of k-tree model was introduced in [7]. The previous experimental results show the huge reduction of retrieval time on a cluster of homogeneous workstations. In this paper, we extend our parallel model to a heterogeneous cluster system environment, by taking into consideration the system characteristics such as computational time, input/output time, available storage, and communication latency. The experiments of the model with load balancing shows a significant reduction of retrieval time while maintaining the quality of perceptual results. Keywords: Data partitioning, Image data...

In this paper, we propose a unified model for indexing and retrieving multimedia data using characteristic features and multiresolution processing. All multimedia data types, such as audio and video, are represented as a k-dimensional signal in a spatio-temporal domain [3]. A k-dimensional signal is transformed into characteristic features and these features are stored in a hierarchical multidimensional structure, called the k-tree. With this approach, both the retrieval accuracy and response time can be dynamically adapted. We present measurements on accuracy and response times for the various levels of the k-tree structure. 1. Introduction Content-based indexing has become more important since conventional databases cannot provide the necessary efficiency and performance. [2] However, it encounters three major difficulties. First, data content (content, or feature) is subjective information that is used to characterize the data. Second, the huge data size affects the computation, s...

In this paper, we propose a parallel, unified model for the indexing and retrieval by content of multimedia data using a weighted cascade. [4] Each multimedia data type can be viewed as k-dimensional (k-d) data in spatiotemporal domain. [3] Each dimension of the data is separated into small blocks and then formed into a multidimensional tree structure, called a k-tree. Using the ktree structure, the retrieval time improves while the retrieval accuracy remains relatively constant. Moreover, since we can realize all types of multimedia data using the same ktree data structure, the data indexing and retrieval algorithms are uniform. In this paper, we demonstrate a parallel algorithm with a weighted cascade that can be used with the uniform model. The parallel processing and weighted cascade algorithm improve the retrieval time. We evaluated the performance results of multimedia database queries using a Beowulf-class cluster of workstations. [1] The experimental results indicate that the ...