Mohand-Said Hacid

A semantic modeling approach for video retrieval by content

A knowledge-based approach to model and retrieve video data by content is developed. Selected objects of interest in a video sequence are described and stored in a database. This database forms the object layer. On top of this layer we define the schema layer used to capture the structured abstractions of the objects stored in the object layer. We propose two abstract languages on the basis of description logics: one for describing the contents of these layers, and the other more expressive, for making queries. The query language provides possibilities for navigation of the schema through forward and backward traversal of links, sub-setting of attributes, and constraints on links.

Representing and Reasoning on Conceptual QueriesOver Image Databases

The problem of content management of multimedia data types (e.g., image, video, graphics) is becoming increasingly important with the development of advanced multimedia applications. Traditional database management systems are inadequate for the handling of such data types. They require new techniques for query formulation, retrieval, evaluation, and navigation. In this paper we develop a knowledge-based framework for modeling and retrieving image data by content. To represent the various aspects of an image objects characteristics, we propose a model which consists of three layers: (1) Feature & Content Layer, intended to contain image visual features such as contours, shapes, etc.; (2) Object Layer, which provides the (conceptual) content dimension of images; and (3) Schema Layer, which contains the structured abstractions of images, i.e., a general schema about the classes of objects represented in the object layer. We propose two abstract languages on the basis of description logics: one for describing knowledge of the object and schema layers, and the other, more expressive, for making queries. Queries can refer to the form dimension (i.e., information of the Feature & Content Layer) or to the content dimension (i.e., information of the Object Layer). These languages employ a variable free notation. As the amount of information contained in the previous layers may be huge and operations performed at the Feature & Content Layer are time-consuming, resorting to the use of materialized views to process and optimize queries may be extremely useful. For that, we propose a formal framework for testing containment of a query in a view expressed in our query language.

Representing and Reasoning on Conceptual Queries Over Image Databases

The problem of content management of multimedia data types (e.g., image, video, graphics) is becoming increasingly important with the development of advanced multimedia applications. In this paper we develop a knowledge-based framework for modeling and retrieving image data. To represent the various aspects of an image object’s characteristics, we propose a model which consists of three layers: (1) Feature and Content Layer, intended to contain image visual features such as contours, shapes, etc.; (2) Object Layer, which provides the (conceptual) content dimension of images; and (3) Schema Layer, which contains the structured abstractions of images. We propose two abstract languages on the basis of description logics: one for describing knowledge of the object and schema layers, and the other, more expressive, for making queries. Queries can refer to the form dimension (i.e., information of the Feature and Content Layer) or to the content dimension (i.e., information of the Object Layer). As the amount of information contained in the previous layers may be huge and operations performed at the Feature and Content Layer are time-consuming, resorting to the use of materialized views to process and optimize queries may be extremely useful. For that, we propose a formal framework for testing containment of a query in a view expressed in our query language.

Model-Based Video Classification toward Hierarchical Representation, Indexing and Access

In this paper, we develop a content-based video classification approach to support semantic categorization, high-dimensional indexing and multi-level access. Our contributions are in four points: (a) We first present a hierarchical video database model that captures the structures and semantics of video contents in databases. One advantage of this hierarchical video database model is that it can provide a framework for automatic mapping from high-level concepts to low-level representative features. (b) We second propose a set of useful techniques for exploiting the basic units (e.g., shots or objects) to access the videos in database. (c) We third suggest a learning-based semantic classification technique to exploit the structures and semantics of video contents in database. (d) We further develop a cluster-based indexing structure to both speed-up query-by-example and organize databases for supporting more effective browsing. The applications of this proposed multi-level video database representation and indexing structures for MPEG-7 are also discussed.

A Rule-Based CQL for 2-Dimensional Tables

We describe the core of a rule-based CQL, devoted to the manipulation of 2- dimensional tabular databases. The rules provide a simple and declarative way to restructure and query tables, and the constraints allow to define cell contents by formulas over concrete domains. We define a model-theoretic semantics and develop an equivalent fixpoint theory that leads to a naive evaluation procedure.

Combining Resolution and Classification for Semantic Query Optimization in DOOD

This paper proposes a framework for semantic query optimization in deductive object-oriented databases. The intentional database is described by means of clauses and a more restricted language is used for the integrity constraints. We apply a specific resolution and a classification mechanism to rewrite a query into a less expensive yet equivalent one. The main contribution of this paper is to show how resolution and classification can be used together within a common framework to perform complementary semantic query optimizations in deductive object oriented databases.

A Knowledge Based Approach for Modeling and Query Multidimensional Databases

In this paper we address the problem of modeling and querying multidimensional databases by exploiting the possibility of using two different languages. Cubes are described within a description logic and the query language is based on a small set of operators used to build cubes from existing ones.

A genetic model for video content based retrieval

Recent progress in compression technology has made it possible for computer to store efficiently pictures, audio and even video. Nevertheless, if such media are widely used in todays communication, efficient computer exploitation is still lacking because automated, high-level interpretation is nowadays impossible. While storing raw video streams in analogic or digital formats is possible, to archive efficiently video documents in order to enable efficient retrieval implies being able to store and manage video content (e.g., raw features, human interpretation of the video document). Recently, this field has received a particular attention. In the past years, two approaches have been considered: (1) systems that use only automated feature extraction, like Jacob [4], and (2) more elaborated systems for concrete applications [3] [1] [5] that integrate user specifications in their video data model.

Modeling and querying video databases

Indexing video data is essential for providing content based access. This paper develops a data model and a rule-based query language for video content based indexing and retrieval. The data model is based on the notion of generalized strata, which can be seen as a set of intervals. Each interval can be analyzed to extract symbolic descriptions of interest that can be put into a database. This database can then be searched to find information of interest. Two types of information are considered: the entities (objects) in the domain of a video sequence; and video frames, called generalized strata, which contain these entities. To represent this information, our data model allows facts as well as objects and constraints. We present a declarative, rule-based, constraint query language that can be used to infer relationships about information represented in the model. In the video applications we are interested in, we wish to construct new generalized strata from old ones. To do this, our language has an interpreted function term (i.e., constructive term) to concatenate generalized strata. The language has a clear declarative and operational semantics.