OBJECT-ORIENTED MODEL FOR GIS COMPRESSED IMAGES Boris Rachev, Mariana Stoeva Technical University of Varna, Department of Computer Science 1, Studentska str., 9010, Varna, Bulgaria

6th EC-GI & GIS Workshop Lyon, France, June Object- oriented Model for GIS Compressed Images Abstract 1. Introduction 2. Definition of the Problem 3. Solution of the Problem 4. Implementation of the OOMCI on the HPPM images 5. Conclusions 6. Acknowledgments

6th EC-GI & GIS Workshop Lyon, France, June Introduction Image bases are imposing to be an essential part of the information systems and multimedia applications that brings their continuous development. Image databases require large storage resource and usually a network information access. Pictorial data model is one of the main problems in Image database systems (IDBS) design and development. Data model has to be extensible, to possess an expressive might and to be able to present image structure and content, the objects it contains and their relationships

6th EC-GI & GIS Workshop Lyon, France, June Definition of the Problem The experience in the domain of creation and utilization of models for interpretation of real world objects images (RW) represented somehow in the formal computer world (CW) shows that a creation of a new model of object oriented type above all, but for representation and retrieval of compressed images is necessary and possible. Such a model has to support direct image search at different levels including spatial search. It also has to be applicable in wide variety of image collections.

6th EC-GI & GIS Workshop Lyon, France, June Solution of the Problem 3.1. Background: Image description 3.2. CIDB Data Models 3.3. Object-Oriented Model for Compressed Images – General description The object oriented model for image data representation was preferred

6th EC-GI & GIS Workshop Lyon, France, June OOMCI Structure Object logical Attributes LOGICAL IMAGE REPRESENTATION PHYSICAL IMAGE REPRESENTATION Image Color Attributes Image Meta Attributes Image Texture Attributes Object texture Attributes Object’s Shape Attributes Image Semantic Attributes Object semantic Attributes Object color Attributes Spatial Object Attributes Object Compression Codes IMAGE SEGMENTATION RELATION DIGITIZED IMAGE SEGMENTED IMAGE SYMBOLIZED IMAGE 1 st level 2 nd level 3 rd level Image Compression Codes

6th EC-GI & GIS Workshop Lyon, France, June Object-Oriented Model for Compressed Images - Example (levels 1,2) IMAGE IMAGE ATTRIBUTES Image Compression Codes 0/0(EOB) / / / st level Color Histogram RGB model 46%,28%,25% Meta Name –Varna/l/33 Date - 01/01/99 Source - reg11,page 32 Semantic Region –Varna Coordinates – ’6” Texture Contrast SEGMENTED IMAGE IMAGE P H R G OBJECTS P H R G ATTRIBUTES Color Average purple Color Average blue Color Average gree Color Average green Texture contrast 1.98 Texture contrast 0.76 Texture contrast 0.35 Texture contrast 1.25 Shape contour175 3 Shape contour Shape contour Shape contour Logical Area 350 m 2 Logical Area 25m 2 Logical Area 115 m 2 Logical Area 500 m 2 Semantic Type -garden Owner-municipality Semantic Type - road Owner-municipality Semantic Type - pond Owner-Petrow Semantic Type - hospital Owner - Petrow 2 nd level Object G Compression Codes ….. Object R Compression Codes ….. Object P Compression Codes ….. Object H Compression Codes …..

6th EC-GI & GIS Workshop Lyon, France, June Object-Oriented Model for Compressed Images - Example (level 3) MBR Y X (45,120) (53,130) (85,127) (123,125) G H P R 3 rd level SYMBOLIZED IMAGE 2-D sting( H1 P<H2<R1<R3<R2<G2< G1; P H2 R2 G2<R3<G1<H1 R1) ORTHOGONAL RELATION G2 H1 H2P R1 R2 G1 R3 OR H1 H2P R1 G2 R2 G1 R3 SPATIAL INDEX RELEVANT POSITION G H R R string (R,H,P,G) SPATIAL INDEX

6th EC-GI & GIS Workshop Lyon, France, June Implementation of the OOMCI on the HPPM images General mathematical description:  w = OOG w (P ij,R ij ), where: OOG is a Object-Oriented variant of the HPPM Model, j=1,N i, i=1,K and R ij are the spatial or simple relationships of the Points P ij. Each set of Points P ij. on each level i must be use as a basis for the image representation and it implementation by OOMCI. Here j is the index of the number of image points, which are situated on the level i.

6th EC-GI & GIS Workshop Lyon, France, June Implementation of the OOMCI on the HPPM real images Two HPPM Points at the level i Two HPPM Sets of Points and two OOMCI representation at the level i+1 Spatial HPPM Relationships “One- to-many” Once One HPPM Point and no one OOMCI represen- tation at the level -  One Full OOMCI representation at the level i Image Compressed Codes … Color Meta Semantic Texture - Color Meta Semantic Texture - Image Compressed Codes … IMAGE 2 ATTRIBUTES IMAGE 1 ATTRIBUTES IMAGE 1 IMAGE 2 ….….

6th EC-GI & GIS Workshop Lyon, France, June Conclusions The proposed model generalized the experience of the existing image data models allowing storage in compressed form. It is used for image representation and retrieval and it is:  Appliable for a great number of collections;  Flexible and may be conformed to the appliance specificity; Supporting direct search not only according alphanumeric attributes, but also according characteristics extracted from the image at different search levels – general image characteristics; object characteristics and spatial characteristics;  Allowing different types of functions on the physical and logical image representation. The CIDB development is directed to: search of algorithms for automatic extraction of data characteristics from the images, composition of structures for spatial data representation and retrieval, improvement of structures description approaches.

6th EC-GI & GIS Workshop Lyon, France, June Acknowledgments This work is supported by the INCO Copernicus project URBAN and includes some proposals, which develop the results of this one. URBAN PARTNERSHIP Epsilon International SA, Greece Municipality Of Bourgas, Bulgaria Municipality Of Varna, Bulgaria Municipality Of Galatzi, Romania Technical University Of Varna, Bulgaria MT-MT Ltd., Bulgaria GEOSYS - Romania ESRI, Germany