Thesis Defense
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Quantifying visual similarity is essential for modeling visual search. Different aspects play different roles in calculating visual similarity. Feature generation is the key problem to solve. The document then describes artificial neural networks and how they imitate signal transmission between neurons. It discusses an overview of visual search and similarity, feature-based quantification methods, and implementations of different feature generation methods like PCA, OBVIS, and SDA on parallel architectures like CUDA and OpenMP.
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Thesis Defense
- 3. QuantifyingVisual Similarity Is Essential for ModelingVisual Search
- 4. Different Aspects Play Different Roles in Calculating Visual Similarity
- 9. Feature Generation Is the Problem Left to Solve
- 10. • Overview • Visual Search andVisual Similarity • Feature BasedVisual Similarity Quantification
- 17. Artificial Neural Network ImitatesTransmission of Signals between Neurons in the Brain
- 18. Forward Propagation: Assign Inputs
- 19. PropagateValues
- 21. Repeat for each subsequent layer
- 22. AssumeTargetValue is 40 and Square Error Is used
- 24. Update Weights Based on Gradients
- 25. Selection of Proper Learning Rate Is Essential
- 30. • Overview • Visual Search andVisual Similarity • Feature BasedVisual Similarity Quantification • Methods for Feature Generation • PCA • ObjectVisual Similarity (OBVIS) • Stacked Denoising Autoencoder (SDA)
- 32. Components In the System. Each parent component is dependent on its child components
- 40. • Overview • Visual Search andVisual Similarity • Feature BasedVisual Similarity Quantification • Methods for Feature Generation • PCA • ObjectVisual Similarity (OBVIS) • Stacked Denoising Autoencoder (SDA) • Implementation and Optimization on Parallel Architecture • CUDA implementation of OBVIS • OpenMP implementation of SDA
- 41. Original Scaling New Scaling New ScalingWeighted
- 43. OBVIS Unweighted OBVISWeighted SDA
- 45. Before ClassificationTraining During ClassificationTraining After ClassificationTraining