Efficient Bitmap-based Indexing and Retrieval of Similarity Search Image Queries

TL;DR

This paper introduces bImageLSH, a bitmap-based index structure that improves the efficiency of similarity search in high-dimensional image datasets while maintaining high accuracy.

Contribution

The paper proposes a novel bitmap-based index structure, bImageLSH, specifically designed for efficient high-dimensional image similarity search.

Findings

Significant performance improvements over existing methods

High accuracy in image retrieval results

Effective handling of high-dimensional image data

Abstract

Finding similar images is a necessary operation in many multimedia applications. Images are often represented and stored as a set of high-dimensional features, which are extracted using localized feature extraction algorithms. Locality Sensitive Hashing is one of the most popular approximate processing techniques for finding similar points in high-dimensional spaces. Locality Sensitive Hashing (LSH) and its variants are designed to find similar points, but they are not designed to find objects (such as images, which are made up of a collection of points) efficiently. In this paper, we propose an index structure, Bitmap-Image LSH (bImageLSH), for efficient processing of high-dimensional images. Using a real dataset, we experimentally show the performance benefit of our novel design while keeping the accuracy of the image results high.