Drawbacks and Proposed Solutions for Real-time Processing on Existing State-of-the-art Locality Sensitive Hashing Techniques

TL;DR

This paper analyzes the limitations of current Locality Sensitive Hashing methods for real-time high-dimensional image data processing and proposes improvements to enhance their efficiency in such applications.

Contribution

The paper identifies drawbacks of existing LSH techniques for real-time data and introduces experimental improvements to address these challenges.

Findings

Existing LSH techniques are inadequate for real-time high-dimensional data.

Proposed improvements enhance the efficiency of LSH in real-time scenarios.

Experimental results demonstrate better performance with the proposed solutions.

Abstract

Nearest-neighbor query processing is a fundamental operation for many image retrieval applications. Often, images are stored and represented by high-dimensional vectors that are generated by feature-extraction algorithms. Since tree-based index structures are shown to be ineffective for high dimensional processing due to the well-known "Curse of Dimensionality", approximate nearest neighbor techniques are used for faster query processing. Locality Sensitive Hashing (LSH) is a very popular and efficient approximate nearest neighbor technique that is known for its sublinear query processing complexity and theoretical guarantees. Nowadays, with the emergence of technology, several diverse application domains require real-time high-dimensional data storing and processing capacity. Existing LSH techniques are not suitable to handle real-time data and queries. In this paper, we discuss the challenges and drawbacks of existing LSH techniques for processing real-time high-dimensional image data. Additionally, through experimental analysis, we propose improvements for existing state-of-the-art LSH techniques for efficient processing of high-dimensional image data.