The generalization of cameras and the increase of storage capacities make data analysis more and more important. Given an image, we need an efficient algorithm to search similar images in a huge dataset. The principle idea of this project is to do quick research in a huge image dataset with help of convolutional neural network(CNN) descriptor. To solve this problem, K-nearest neighbour algorithm (KNN) is widely used. In this project, Locality Sensitive Hashing(LSH) and Dynamic Continuous Indexing(DCI) are implemented and tested.
- Python 2.7, Scipy, Numpy
- g++ (the version should support c++ 11)
- Calculate image discriptors by convolutional neural network (Python + Keras)
- Implement LSH based on different distances (Cosine distance, Jaccard distance, Hamming distance, Euclidean distance) (C++)
- Implement DCI (C++)