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DOG I : an Annotation System for Images of Dog Breeds

DOG I : an Annotation System for Images of Dog Breeds. Antonis Dimas Pyrros Koletsis Euripides Petrakis Intelligent Systems Laboratory Technical University of Crete (TUC) Chania, Crete, Greece. Image Annotation. The task of assigning a name or description to an unknown image

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DOG I : an Annotation System for Images of Dog Breeds

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  1. DOGI: an Annotation System for Images of Dog Breeds Antonis Dimas Pyrros Koletsis Euripides Petrakis Intelligent Systems Laboratory Technical University of Crete (TUC) Chania, Crete, Greece

  2. Image Annotation • The task of assigning a name or description to an unknown image • Manual: good quality, but slow, subjective • Automatic: classification problem, relies on associating image analysis features with high level concepts • Difficult to handle all image types • Semantic gap: map features to classes ICIAR 2012, Aveiro, Portugal

  3. DOGI: http://www.intelligence.tuc.gr/prototypes.php • An automatic image annotation system for images of dog breeds • 40 classes (dog breeds) • Descriptions: information in an ontology • Class names, properties, features, textual descriptions (from WordNet, Wikipedia) • Annotations in MPEG7 ICIAR 2012, Aveiro, Portugal

  4. DOGI: System Load Image Select ROI Annotation Method Graphical User Interface (GUI) MPEG7 features Color + Texture features Images: 12-dim vectors Feature Extraction 40 classes 9 instances/class Class hierarchy Class properties DOGI Ontology Ontology Mapping Image Retrieval Select Annotation Mathod Store Annotation in Exif header Image Annotation ICIAR 2012, Aveiro, Portugal

  5. Select ROI ICIAR 2012, Aveiro, Portugal

  6. Image Content Analysis • Images of dog breeds are mainly characterized by the spatial distribution of color intensities • A 12-dimension feature vector of Color, Texture, Hybrid feature from LIRE Library • Features are normalized in [0,1] • Not all features are equally important ICIAR 2012, Aveiro, Portugal

  7. Ontology • 40 classes of dog breed organized in IS_A hierarchy • E.g., Dog  Working Group  Saint Bernard • Three separate hierarchies for text, features and visual descriptions • 9 instances per class: raw images + a 12-dim feature vector for each image in class ICIAR 2012, Aveiro, Portugal

  8. DOGI Ontology ICIAR 2012, Aveiro, Portugal

  9. Image Annotation • The unknown image Q is compared with each one of the 360 images in the ontology • D(Q,I) = Σi widi(Q,I) • Results are ranked by similarity with Q • Weights wi are computed by decision trees • Training set of 3,474 image pairs ICIAR 2012, Aveiro, Portugal

  10. Annotation Method • Best Match: Select class of most similar instance • Max Occurrence: Select class with more instances in the first 20 answers • Average Retrieval Rank: Select class with instances ranked higher in the first 20 answers • Max Similarity: Select class whose instancing sum-up to max similar score ICIAR 2012, Aveiro, Portugal

  11. Example Image ICIAR 2012, Aveiro, Portugal

  12. Annotation Result ICIAR 2012, Aveiro, Portugal

  13. EXIF Metadata • Descriptive information embedded inside an image • The metadata captured by your camera is called EXIF data .. • DOGI stores annotation info with the pictures in the EXIF • Can be useful for image archiving and later retrievals ICIAR 2012, Aveiro, Portugal

  14. Annotation in MPEG7 ICIAR 2012, Aveiro, Portugal

  15. Evaluation • Average annotation accuracy over 40 queries ICIAR 2012, Aveiro, Portugal

  16. Conclusions-Future Work • DOGΙ: An automatic annotation system for dog breeds with good performance • Useful as a tool for many application • Annotation accuracy improves for less categories • Experimenting with more and animal species images categories • More elaborate image classification methods ICIAR 2012, Aveiro, Portugal

  17. Thank YOU !! ICIAR 2012, Aveiro, Portugal

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