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Nearest-neighbor query processing is a fundamental operation for many imageretrieval applications. Often, images are stored and represented byhigh-dimensional vectors that are generated by feature-extraction algorithms.Since tree-based index structures are shown to be ineffective for highdimensional 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 approximatenearest neighbor technique that is known for its sublinear query processingcomplexity and theoretical guarantees. Nowadays, with the emergence oftechnology, several diverse application domains require real-timehigh-dimensional data storing and processing capacity. Existing LSH techniquesare not suitable to handle real-time data and queries. In this paper, wediscuss the challenges and drawbacks of existing LSH techniques for processingreal-time high-dimensional image data. Additionally, through experimentalanalysis, we propose improvements for existing state-of-the-art LSH techniquesfor efficient processing of high-dimensional image data.

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