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Object- oriented Model for GIS Compressed Images

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. Object- oriented Model for GIS Compressed Images. Abstract 1. Introduction 2. Definition of the Problem

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Object- oriented Model for GIS Compressed Images

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  1. OBJECT-ORIENTED MODEL FOR GIS COMPRESSED IMAGESBoris Rachev, Mariana StoevaTechnical University of Varna, Department of Computer Science1, Studentska str., 9010, Varna, Bulgaria

  2. 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, 28-30 June 2000

  3. 1. 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, 28-30 June 2000

  4. 2. 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, 28-30 June 2000

  5. 3. 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, 28-30 June 2000

  6. PHYSICAL IMAGE REPRESENTATION LOGICAL IMAGE REPRESENTATION IMAGE SEGMENTATION RELATION Image Meta Attributes Image Color Attributes Image Texture Attributes Image Semantic Attributes Image Compression Codes DIGITIZED IMAGE 1st level Object Compression Codes SEGMENTED IMAGE Object color Attributes Object texture Attributes Object semantic Attributes Object logical Attributes Object’s Shape Attributes 2nd level SYMBOLIZED IMAGE Spatial Object Attributes 3rd level OOMCI Structure 6th EC-GI & GIS Workshop Lyon, France, 28-30 June 2000

  7. SEGMENTED IMAGE IMAGE H G H Logical Area 350 m2 R P P R G P Logical Area 25m2 Logical Area 115 m2 Logical Area 500 m2 IMAGE ATTRIBUTES Color Histogram RGB model 46%,28%,25% Meta Name –Varna/l/33 Date - 01/01/99 Source - reg11,page 32 Image Compression Codes 0/0(EOB) 1010 0/1 0000 0/2 0101 0/3 1011 Texture Contrast - 0.84 Semantic Region –Varna Coordinates – 430 8’6” ATTRIBUTES OBJECTS Color Average purple Texture contrast 1.98 Shape contour 2064242464 Semantic Type - hospital Owner - Petrow IMAGE Color Average blue Texture contrast 0.76 Shape contour 21076543 Semantic Type - pond Owner-Petrow Semantic Type - road Owner-municipality Color Average gree Texture contrast 0.35 Shape contour 27105313 Object G Compression Codes Object P Compression Codes Object R Compression Codes Object H Compression Codes Semantic Type -garden Owner-municipality Color Average green Shape contour1753 Texture contrast 1.25 0100101….. 0100101….. 0100101….. 0100101….. 2nd level 1st level Object-Oriented Model for Compressed Images - Example (levels 1,2) 6th EC-GI & GIS Workshop Lyon, France, 28-30 June 2000

  8. RELEVANT POSITION R string (R,H,P,G) ORTHOGONAL RELATION SPATIAL INDEX OR G1 H1 R3 R1 H1 R1 G1 P H2 R2 G2 P H2 R2 G2 Y H (53,130) (85,127) G (123,125) R3 (45,120) P R X SYMBOLIZED IMAGE 2-D sting( H1 P<H2<R1<R3<R2<G2< G1; P H2 R2 G2<R3<G1<H1 R1) SPATIAL INDEX H 3rd level MBR G R Object-Oriented Model for Compressed Images - Example (level 3) 6th EC-GI & GIS Workshop Lyon, France, 28-30 June 2000

  9. 4. Implementation of the OOMCI on the HPPM images General mathematical description: w = OOGw(Pij,Rij), where: OOG is a Object-Oriented variant of the HPPM Model, j=1,Ni, i=1,K and Rij are the spatial or simple relationships of the Points Pij. Each set of Points Pij. 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, 28-30 June 2000

  10. IMAGE 1 ATTRIBUTES IMAGE 1 Color Semantic Meta Texture - Spatial HPPM Relationships “One-to-many” Once One HPPM Point and no one OOMCI represen- tation at the level -  Two HPPM Sets of Points and two OOMCI representation at the level i+1 …. Two HPPM Points at the level i One Full OOMCI representation at the level i Image Compressed Codes Image Compressed Codes ….. 01100 ….. 0011 Color Semantic Meta Texture - IMAGE 2 IMAGE 2 ATTRIBUTES 4. Implementation of the OOMCI on the HPPM real images 6th EC-GI & GIS Workshop Lyon, France, 28-30 June 2000

  11. 5. 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, 28-30 June 2000

  12. 6. Acknowledgments This work is supported by the INCO Copernicus project URBAN 960252 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 6th EC-GI & GIS Workshop Lyon, France, 28-30 June 2000

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