A Fast Word Retrieval Technique Based on Kernelized Locality Sensitive Hashing

In this paper, we have presented a new and faster word retrieval approach, which is able to deal with heterogeneous document image collections. A certain amount of image features (statistical and Gabor Wavelet) are extracted, which inherently represent word´s images. These features are used for generating hash table for fast retrieval of similar image from a very large image dataset. The decomposition and embedding of high-dimensional features and complex distance functions into a low-dimensional Hamming space helps to efficiently search items. However, existing methods do not apply for high-dimensional kernelized data when the underlying features´ embedding for the kernel is unknown. The generalization of locality sensitive hashing (LSH) for arbitrary kernel is presented in the paper. The proposed algorithm provides sub-linear time similarity search and works for a wide class of similarity functions.