Abstract
Hashing for similarity search in large scale data has become an increasingly popular technique. K-means Hashing (KMH) has been proven effective because of the benefits of adaptive k-means quantization. However, KMH is a batch-based learning model requiring high time and storage complexities, which makes it hard to load large scale data into memory to train and deal with streaming data. To address this problem, in this paper we propose an online hashing method using Self-Organizing Map (SOM) algorithm, named as Online Self-Organizing Hashing (SOH). Specifically, we map the training data to an affinity preserving hyper-cube with each vertex assigned a binary code using a SOM alike algorithm. After training, a new data point is quantized into a vertex of the hyper-cube and encoded into related binary code. Experimental results demonstrate that SOH has better or comparable retrieval performance to various state-of-the-art hashing methods while simultaneously requiring rather low computational complexity and storage space.