A comparative study of hash based approximate nearest neighbor learning and its application in image retrieval

Plenty of data are available due to the growth of digital technology that creates a high expectation in retrieving the relevant images, accurately and efficiently for a given query image. For searching the relevant images efficiently for the Large Scale dataset, the searching algorithm should have fast access capability. The existing Exact Nearest Neighbor search performs in linear time and so it takes more time as both the dataset and data dimension increases. As a remedy to provide sub-linear/logarithmic time complexity, usage of Approximate Nearest Neighbor (ANN) algorithms is emerging at a rapid rate. This paper discusses about the importance of ANN and their general classification; the different categories involved in Learning to Hash has been analyzed with their pros and cons; different bit assignment types and methods to minimize the Quantization Errors have been reviewed along with its merits and demerits. Therefore, it serves to increase the efficiency of the Image Retrieval process in Large Scale.