Shi-Kuo Chang. Principles of Pictorial Information Systems Design, chapter 5, pages 82--121. Prentice Hall International Editions, 1989.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....technology to handle image data. The physical database is implemented as a separate image store holding the original image files. Pointers are implemented from the logical database to the images stored in the physical database. Prior to storing physical images, image compression techniques [36] can be applied to reduce storage requirements. Recent proposals regarding the design of IDB systems and the management of image data are influenced by the object oriented approach [37, 4, 38, 39] This approach offers a framework within which different types of entities (e.g. different kinds ....

Shi-Kuo Chang. Principles of Pictorial Information Systems Design, chapter 5, pages 82--121. Prentice Hall International Editions, 1989.

....work, and three algorithms for 2 D string matching are presented in Appendix A. Finally, a description of the user interface is given in Appendix B. Chapter 2 Related Work 2. 1 Introduction ISSUES related to IDB system design and implementation have been raised previously by many investigators [14, 1, 15, 16]. However, a general methodology for the design of such systems has not been developed so far. Only when the requirements of a particular application domain have been determined, techniques of image analysis, description, and image content representation, with known database design methods, are ....

Shi-Kuo Chang. Principles of Pictorial Information Systems Design. Prentice Hall International Editions, 1989.

....the derived image subsets. In particular, indexing is performed by computing an address, first to each individual attribute string and then to each ordered subset as a whole. 5 The IDB consists of two parts: the physical database which stores the original image files and the logical database [10] which stores the image subsets together with their representations. The logical database consists of a set of data files each storing subsets of equal size. Each data file is partitioned in segments (data pages) of fixed capacity, each corresponding to a unique address. Each subset is stored ....

....features and mechanisms are inherent within classical DBMSs such as the relational ones [34] Earlier approaches to IDB systems design distinguish image data into: original image files, called physical images , and image related data (i.e. descriptions, attributes etc. called logical images [35, 10]. Physical and logical images are stored into a physical and a logical database respectively. There may be a need to treat physical and logical data independently since, in most cases, they serve different purposes. Access methods are usually applied to 16 logical data rather than the vast amount ....

[Article contains additional citation context not shown here]

Shi-Kuo Chang. Principles of Pictorial Information Systems Design, chapter 8, pages 172--211. Prentice Hall International Editions, 1989.

....can also be objects. Object classes are organized into hierarchies thus, taking advantage of the property of inheritance . Image data can be distinguished into: original image files, called physical images , and image related data (i.e. descriptions, attributes etc. called logical images [3]. Physical and logical images are stored into a physical and a logical database respectively. There may be a need to treat physical and logical data independently since, in most cases, they serve different purposes. Access methods are usually applied to logical data rather than the vast amount of ....

Shi-Kuo Chang. Principles of Pictorial Information Systems Design, chapter 8, pages 172--211. Prentice Hall International Editions, 1989. 31

....value in the range [0# 2] The string encoding the size (area) property of the objects contained in the ordered image subset (4 2 5 3) is (1 2 0 0) while the string encoding the roundness property is (1 2 2 2) 3. 4 Image Storage Image data can be distinguished into physical and logical [15]. Original (grey level) images, segmented images, and image miniatures are physical images. On the other hand, image related data (information extracted from images, attributes, text etc. are logical images. Physical and logical images are stored separately in a physical and a logical database ....

Shi-Kuo Chang. Principles of Pictorial Information Systems Design, chapter 8, pages 172--211. Prentice Hall International Editions, Englewood Cliffs, New Jersey 07632, 1989.

....(eyes, nose, etc. and their attributes and relative positions are computed. 2.1 Background Image descriptions are given in terms of object properties and in terms of relationships between objects. The textbook approach to capture this information is the Attributed Relational Graphs (ARGs) [3, 4]. In an ARG, the objects are represented by graph nodes and the relationships between objects are represented by arcs between such nodes. Both nodes and arcs are labeled by attributes corresponding to properties (features) of objects and relationships respectively. Figure 1 shows an example image ....

....are: Image feature extraction, image content representation and organization of stored information, search and retrieval strategies, and user interface design. Addressing such issues has become object of intensive research activities in many areas of Computer Science over the past few years [7, 4, 8]. Advances mainly in the areas of Databases and Computer Vision research resulted in methods which can be used for image archiving, retrieval and IDB design work. However, as observed in [9] there is a need for increased communication between the vision and the database communities to deal with ....

[Article contains additional citation context not shown here]

Shi-Kuo Chang. Principles of Pictorial Information Systems Design. Prentice Hall International Editions, 1989.

....difficult due to the noise present in the input as well as the enormous size of the search space. For many applications however, it is sufficient to derive more modest symbolic representations of visual data. Examples of this include colour histograms, texture measures as well as skeletons [Cha89] NI94] reports on an image retrieval system based on colour indexing where users communicate their queries in English. Words such as darker are interpreted based on the colour model being employed. Significant work is being conducted in deriving such representations by the image database ....

Shi-Kuo Chang. Principles of Pictorial Information Systems Design. Prentice-Hall, 1989.

....technology to handle image data. The physical database is implemented as a separate image store holding the original image files. Pointers are implemented from the logical database to the images stored in the physical database. Prior to storing physical images, image compression techniques [36] can be applied to reduce storage 11 requirements. Recent proposals regarding the design of IDB systems and the management of image data are influenced by the object oriented approach [37, 4, 38, 39] This approach offers a framework within which different types of entities (e.g. different ....

Shi-Kuo Chang. Principles of Pictorial Information Systems Design, chapter 5, pages 82--121. Prentice Hall International Editions, 1989.

....work, and three algorithms for 2 D string matching are presented in Appendix A. Finally, a description of the user interface is given in Appendix B. Chapter 2 Related Work 2. 1 Introduction ISSUES related to IDB system design and implementation have been raised previously by many investigators [14, 1, 15, 16]. However, a general methodology for the design of such systems has not been developed so far. Only when the requirements of a particular application domain have been determined, techniques of image analysis, description, and image content representation, with known database design methods, are ....

Shi-Kuo Chang. Principles of Pictorial Information Systems Design. Prentice Hall International Editions, 1989.

....all the derived image subsets. In particular, indexing is performed by computing an address, first to each individual attribute string and then to each ordered subset as a whole. The IDB consists of two parts: the physical database which stores the original image files and the logical database [10] which stores the image subsets together with their representations. The logical database consists of a set of data files each storing subsets of equal size. Each data file is partitioned in segments (data pages) of fixed capacity, each corresponding to a unique address. Each subset is stored ....

....features and mechanisms are inherent within classical DBMSs such as the relational ones [34] Earlier approaches to IDB systems design distinguish image data into: original image files, called physical images , and image related data (i.e. descriptions, attributes etc. called logical images [35, 10]. Physical and logical images are stored into a physical and a logical database respectively. There may be a need to treat physical and logical data independently since, in most cases, they serve different purposes. Access methods are usually applied to logical data rather than the vast amount of ....

[Article contains additional citation context not shown here]

Shi-Kuo Chang. Principles of Pictorial Information Systems Design, chapter 8, pages 172--211. Prentice Hall International Editions, 1989.

....etc. and their attributes and relative positions are computed. 2.1 Background Image descriptions are given in terms of object properties and in terms of relationships between objects. Such image descriptions are represented by relational structures such as Attributed Relational Graphs (ARGs) [4, 11]. In an ARG, the objects are represented by graph nodes and the relationships between objects are represented by arcs between such nodes. Both nodes and arcs are labeled by attributes corresponding to properties (features) of objects and relationships respectively. left eye object 3 right eye nose ....

....are: Image feature extraction, image content representation and organization of stored information, search and retrieval strategies, and user interface design. Addressing such issues has become object of intensive research activities in many areas of Computer Science over the past few years [57, 11, 35, 13]. Advances mainly in the areas of Databases and Computer Vision research resulted in methods which can be used for image archiving, retrieval and IDB design work. However, as observed in [32] there is a need for increased communication between the vision and the database communities to deal with ....

[Article contains additional citation context not shown here]

Shi-Kuo Chang. Principles of Pictorial Information Systems Design. Prentice Hall International Editions, 1989.

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