Bloom Filters and Compact Hash Codes for Efficient and Distributed Image Retrieval

This paper presents a novel method for efficient image retrieval, based on a simple and effective hashing of CNN features and the use of an indexing structure based on Bloom filters. These filters are used as gatekeepers for the database of image features, allowing to avoid to perform a query if the query features are not stored in the database and speeding up the query process, without affecting retrieval performance. Thanks to the limited memory requirements the system is suitable for mobile applications and distributed databases, associating each filter to a distributed portion of the database. Experimental validation has been performed on three standard image retrieval datasets, outperforming state-of-the-art hashing methods in terms of precision, while the proposed indexing method obtains a $2\times$ speedup

Hybrid Hashing Method for Similar Vehicle Image Search

The novel hybrid method of a hash image calculation that can be applied in a search for similar vehicle images is proposed in this paper. The main novelty of the method described herein is the combination of two hashing types: the visual and semantic hash of the image. The method is based on SIFT and DCT algorithms. We use frontal vehicle images to test the method accuracy. The experimental results indicate that the proposed algorithm has the practical application of image search in the vehicle identification systems based on license plate recognition. We show that method is a novel in this area. The proposed method is also applicable for use in other problem domains