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Iterative and Interactive Search for Objects

This study presents an innovative framework for iterative and interactive object search utilizing advanced database organization and feature extraction techniques. By employing the Euclidean and weighted distance metrics, the method refines search results based on user feedback, categorizing objects effectively through distinct visual features. Utilizing both 2D and 3D representations, data is normalized, iconized, and structured hierarchically, enabling streamlined searching with targeted parameters. The framework showcases significant improvements in identifying object classes and sets the stage for further exploration of feature integration.

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Iterative and Interactive Search for Objects

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  1. Iterative and Interactive Search for Objects Moty Golan & Oren Kerem Instructor: Dr. Sigal Ar

  2. Choose object Search Mark Results Introduction • Databases of objects • choose one working database • choose object from the database • arrange database by similarity to chosen object • Interactive: results can be marked as “good” or “bad” • Iterative: marked results used to refine the search

  3. Databases • 3D objects’ surface • 10000 sampling points (no color) • Uniform distribution • Location of point and normal to the surface • 2D color pictures • Distinct families of pictures • HSV color representation

  4. Objects’ Metric • Object representation - feature vector • Initial distance between objects - Euclidean distance: d(Dx , Dy) = || X - Y ||2 • Iterations: weighted Euclidean distance

  5. Weights • Weighted distance : d(Dx , Dy) = [X - Y ]T W [X - Y] + b

  6. Feature Vectors • Data of a certain type is extracted from the object • A vector of values is calculated based on the data • Feature type – the method of the values calculation • Moments • Histograms

  7. Data Types • One aspect of the object is represented by a finite set of values • Naturally numerous aspects exist • For graphic object use visual aspects: • Pixels • Normals • Curvature • Color (HSV representation)

  8. 3D 2D Moments Histograms Moments Histograms points normals points normals curvature HSV points curvature HSVpoints Implemented Data and Feature Types

  9. Goals • Organize the databases • Design an interface to enable searches on both databases using all parameters • Conduct searches to explore behavior

  10. Organize_3dBase_Mom Read_Graphic_Body Extract_MomFeatures Create_Icon Compute_Moments Organizing the Databases • Extracting the data types • Calculation of feature vectors • Create object icons • Build directory hierarchy 3D Example:

  11. Calculating Features • Normalize the data if needed • Ex. according to 1st & 2nd order moments • Calculate moment vectors according to desired orders • Calculate and flatten histograms • Save in appropriate files

  12. Icons • “Flatten” and scale the 3D objects • Sub-sample the 2D objects

  13. root sample 3d features images 2d 2d 3d 2d 3d 2d 3d Organized Databases

  14. Interface Options • Select database: 2D / 3D • Choose object • Set search parameters • Feature kind and level • Data type • Conduct search • Database viewing & browsing • Full data display • Good and bad object marking

  15. STARTING POINT Choose Object Button Program FlowDiagram CHOOSE OBJECT Choose Object Button ANYWHERE / Change Database Choose This Button WAITING TO SEARCH Change search parameters Search Button WAITING FOR MARKS Update Search Button Mark Results Button MARKING OBJECTS

  16. Implementation: State Machine • Each program stage is a state: • Program waiting stages • Program action stages • User actions switch between states • In each waiting state the relevant options are made available while switch case a: ... case b: ... . . . end end

  17. Legend empty command case user action INITIAL CASE 100 Cases Diagram … Choose This pressed Change Object / Database case 1 Change Parameters … 10 ANYWHERE Search pressed case 3 30 Update Search pressed case 2 Mark Results pressed Good Object pressed case 23 case 5 Bad Object pressed 50 case 25

  18. Demonstration

  19. Test Runs • Good results • Most tests converged within 3-4 iterations • Increase in “good” objects in each iteration • “Bad” objects removed from top 30 results • Limitations • Database variety • Computation demands vs. response time • Human perception vs. object representation

  20. Feature Explorations • Higher feature levels do not guarantee better results • Completeness of representation does not guarantee better results • Some tests affected by feature type

  21. Conclusions • Database arrangement and interface were implemented as shown • Various searches show that the algorithm successfully identifies classes of objects • Further research can include: • The effect of specific high feature levels • The effect of computation constants • Combining different feature types and levels

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