Landmark_Recognition

• Question: Given a photo, can we recognize the correct landmarks it contains?

One-shot learning

An object categorization problem in computer vision. Whereas most machine learning based object categorization algorithms require training on hundreds or thousands of images and very large datasets, one-shot learning aims to learn information about object categories from one, or only a few, training images.

Characteristics:

• A few training images for each object/class
• Potentially large amount of objects/classes

Application

• Face/Item Recognition/Verification
• Street-to-Shop Systems
• Landmark Recognition

Landmark Recognition

Datasets

• Google Landmark Recognition Dataset
  • 1,225,029 training images with 14,951 landmarks
  • 117,703 test images
  • Image aren't evenly distributed

• Subset Dataset for this work
  • 113,783 training images with 14,943 different landmarks
  • 22,255 validation images with 7675 different landmarks
  • 22,391 test images with 14436 different landmarks

Metric Learning

Triplet Network

• Fine-tuning with pre-trained models
  • VGG16, InceptionV3, and ResNet have been well trained
  • Lower layers usually encode more generic, reusable features
  • Higher layers encode more specialized features
  • Freeze lower layers and only train the top several layers

Prediction

Results

• With fine-tuned InceptionV3 Triplet Network, top 1 accuracy is 47%