Indexing Large Visual Vocabulary by Randomized Dimensions Hashing for High Quantization Accuracy: Improving the Object Retrieval Quality

Abstract

The bag-of-visual-words approach, inspired by text retrieval methods, has proven successful in achieving high performance in object retrieval on large-scale databases. A key step of these methods is the quantization stage which maps the high-dimensional image feature vectors to discriminatory visual words. In this paper, we consider the quantization step as the nearest neighbor search in large visual vocabulary, and thus proposed a randomized dimensions hashing (RDH) algorithm to efficiently index and search the large visual vocabulary. The experimental results have demonstrated that the proposed algorithm can effectively increase the quantization accuracy compared to the vocabulary tree based methods which represent the state-of-the-art. Consequently, the object retrieval performance can be significantly improved by our method in the large-scale database.