Project 2 SIFT Matching by Hierarchical K-means Quantization

Experiment Setting Image database – UKBench dataset: images from 2550 categories Feature extraction – SIFT feature The source code will be provided –

Tasks Codebook training – Sample SIFT features from the UKB data set Training sample number: 100K – Train visual codebook by hierarchical k-means Codebook size: 10K (level = 4, branch = 10) – Each cluster center is regarded as a visual word Feature quantization with visual codebook – Identify the closest visual word for a test feature – Assign the cluster ID to the feature as quantization result Feature matching based on quantization – Two features from two images are considered as a match if they are quantized to the same visual word – Select 5 relevant image pairs to conduct feature matching – Select 5 irrelevant image pairs to conduct feature matching

Implementation Program with C++ or Matlab – You may need Open CV when programing with C++ OpenCV 210 library files are provided – You do not need to install the OpenCV source file Refer to OpenCV China for instructions to set programming environment – Refer to OpenCV China for instructions to process images : –