Implementing Dog Query by Photo System for Pet Care and Lost Dog Recovery

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Explore the implementation of a system utilizing Type-M Functional Dependencies to organize digital archives based on visual similarities, primarily focusing on retrieving dogs from multimedia databases through photo queries. The system's components, such as the GUI, DogRec Monitor, and messaging protocols, facilitate dog breed identification and data exchange between users and the system. Various distance functions and classifiers enhance the query process, making it efficient for pet care and lost dog recovery scenarios.

  • Dog Query
  • Pet Care
  • Multimedia Database
  • Lost Dog Recovery
  • Distance Functions
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  1. DOG QUERY BY PHOTO MS1 Wen-Chyi Lin CS2310 Software Engineering

  2. MOTIVATION Never express yourself more clearly than you are able to think by Niels Bohr. However, there are times and situations we imagine what we desire, but are unable to express it in precise wording. Type-M Functional Dependencies (MFDs) can assist to organize digital archives (video, image, sound, ) by their visual or auditory similarities (patterns). Using a photo to retrieve the dog from the multimedia database will be helpful for pet care as well as finding lost dogs through street surveillance cameras.

  3. THE SYSTEM Universal Interface M31 M32 Video Sensor DogRec Monitor GUI SIS Server

  4. MESSAGES Gui DogRecMonitor Description: Create GUI Component Description: Create DogRec Monitor Component MsgID:20 MsgID:20 Variables: Passcode: **** SecurityLevel: 3 Name: GUI SourceCode: Gui.jar InputMsgID 1: 1002 (Dog Data Stream) OutputMsgID 1: 1001 (DogRec Monitor Enable) OutputMsgID 2: 22 (Kill Component) Component Description: GUI displays the vital messages and manages SIS Variables: Passcode: **** SecurityLevel: 3 Name: DogRecMonitor SourceCode: DRM.jar InputMsgID 1: 1001 (DogRec Monitor Enable) OutputMsgID 1: 1002 (Dog Data Stream) Component Description: DogRec Monitor checks for dog breed on the queried photo and generates a message when one is found.

  5. MESSAGES Gui DogRecMonitor

  6. MESSAGES GUIToDog DogToGUI

  7. THE SCENARIO DogRec Monitor Msg: GUIToDog Msg: DogToGUI User Msg: GUIToDog Query results SIS Server GUI Msg: DogToGUI

  8. METHOD AND DISTANCE FUNCTION screen response Dog Photo Query Haar cascade classifier Object Detect Distance Function EigenFace(PCA) FisherFace(LDA) LBPH DB Features Extraction User Find Dog Type

  9. INTERMEDIATE RESULTS Haar cascade classifier EigenFace(PCA) Eigenface (1) Eigenface (2) Eigenface (3) Eigenface (4) FisherFace(LDA) Local Binary Patterns Histograms (LBPH) LBPH Face Fisherface (1) Fisherface (2) Fisherface (3) 60 40 20 0 Stanford Dogs Dataset

  10. REFERENCES Datta, Ritendra, et al. "Image retrieval: Ideas, influences, and trends of the new age." ACM Computing Surveys (CSUR) 40.2, 2008. Shi-Kuo Chang; Deufemia, V.; Polese, G.; Vacca, M., "A Normalization Framework for Multimedia Databases," Knowledge and Data Engineering, IEEE Transactions on , vol.19, no.12, pp.1666,1679, Dec. 2007. Paul Viola and Michael J. Jones. Rapid Object Detection using a Boosted Cascade of Simple Features. IEEE CVPR, 2001. Belhumeur, Peter N., Jo o P. Hespanha, and David Kriegman. "Eigenfaces vs. fisherfaces: Recognition using class specific linear projection." Pattern Analysis and Machine Intelligence, IEEE Transactions on 19.7 (1997): 711-720. Ahonen, Timo, Abdenour Hadid, and Matti Pietikainen. "Face description with local binary patterns: Application to face recognition." Pattern Analysis and Machine Intelligence, IEEE Transactions on 28.12 (2006): 2037-2041. http://docs.opencv.org/trunk/modules/contrib/doc/facerec/tutorial/facerec_vide o_recognition.html#aligning-face-images http://vision.stanford.edu/aditya86/ImageNetDogs/

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