Hashing based feature aggregating for fast image copy retrieval

Recently the methods based on visual words have become very popular in near- duplicate retrieval and content identification. However, obtaining the visual vocabulary by quantization is very time-consuming and unscalable to large databases. In this paper, we propose a fast feature aggregating method for image representation which uses machine learning based hashing to achieve fast feature aggregation. Since the machine learning based hashing effectively preserves neighborhood structure of data, it yields visual words with strong discriminability. Furthermore, the generated binary codes leads image representation building to be of low-complexity, making it efficient and scalable to large scale databases. The evaluation shows that our approach significantly outperforms state-of-the-art methods.