Pictorial Database Systems Research Articles

The consistency problem of content-based pictorial description in pictorial database systems

The consistency problem of content-based pictorial description in pictorial database systems

Reasoning about Qualitative Spatial Relationships

In this paper, we consider various spatial relationships that are of general interest in pictorial database systems and other applications. We present a set of rules that allow us to deduce new relationships from a given set of relationships. A deductive mechanism using these rules can be used in query-processing systems that retrieve pictures by content. The given set of rules is shown to be i>sound; that is, the deductions are logically correct. The rules are also shown to be i>complete for three-dimensional systems; that is, every relationship that is implied by a given consistent set of relationships i>F is deducible from i>F using the given rules. In addition, we show that the given set of rules is incomplete for two-dimensional systems. We also present efficient algorithms for the deduction and reduction problems. The deduction problem consists of computing all the relationships deducible from a given set, while the reduction problem consists of computing a minimal subset of a given set of relationships that implies all the relationships in the given set.

Dynamic pictorial databases design for similarity retrieval

A polynomial time dynamic method for similar retrieval in pictorial database systems is presented. Although several algorithms have been published, they all focus on static data sets. With regard to applications in a dynamic environment, they all seem too obtuse to be used efficiently. To overcome this defect, we have to utilize some dynamic data structures. The well-known trie hashing and extendible hashing techniques are what we employ. Both of them have proven to have good performance in database management systems. From a practical point of view, it is feasible to transplant these mechanisms into pictorial database systems.

Automatic indexing and content-based retrieval of captioned images

The interaction of textual and photographic information in an integrated text/image database environment is being explored. Specifically, our research group has developed an automatic indexing system for captioned pictures of people; the indexing information and other textual information is subsequently used in a content-based image retrieval system. Our approach presents an alternative to traditional face identification systems; it goes beyond a superficial combination of existing text-based and image-based approaches to information retrieval. By understanding the caption accompanying a picture, we can extract information that is useful both for retrieving the picture and for identifying the faces shown. In designing a pictorial database system, two major issues are (1) the amount and type of processing required when inserting new pictures into the database and (2) efficient retrieval schemes for query processing. Our research has focused on developing a computational model for understanding pictures based on accompanying descriptive text. Understanding a picture can be informally defined as the process of identifying relevant people and objects. Several current vision systems employ the idea of top-down control in picture understanding. We carry the notion of top-down control one step further, exploiting not only general context but also picture-specific context.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

A hypertext query language for images

HYPERQUERY is a hypertext query language for object-oriented pictorial database systems. First, we discuss object calculus based on term rewriting. Then, example queries are used to illustrate language facilities. This query language has been designed with a flavor similar to QBE as the highly nonprocedural and conversational language for object-oriented pictorial database management system OISDBS.

Model-based remotely-sensed imagery interpretation

Abstract We present a model-based remotely-sensed image interpretation expert system embeded in a knowledge-based geographic information system (K. BIS). The KBIS consists of four sub-systems: a pictorial data base system, an image interpretation expert system, a computer-aided planning system and a computer-aided cartographic system. The image interpretation expert system represents ecological knowledge and other expert knowledge by frames. Its reasoning process consists of a forward reasoning based on the Bayes classification of Landsat imagery, a backward reasoning using frame knowledge and reasoning using a spatial consistency model. A forest inventory study was conducted in Shaxian county, in the southern part of China, using this expert system. The results have shown a significant improvement. Building image interpretation expert systems within knowledge-based pictorial systems is very convenient and efficient because there are well-organized data, knowledge and procedures available.

Pictorial Data-Base Systems

New applications in digital picture processing—notably in handling geographic data and in computer-aided design—have prompted development of a variety of pictorial data-base systems.

Picture Query Languages for Pictorial Data-Base Systems

Integrated pictorial data-base systems need pictorial query languages to enhance their effectiveness. The tabular structures of QPE, IQ, and others are easy to understand and access.