Adaptive Feature-Aware Hashing (AFAH): A Lightweight Data-Driven Hashing Framework for Efficient Image Retrieval

In large scale image retrieval and big data analytics it is a big challenge to search similar images from high dimensional data. Mostly used algorithms are Locality Sensitive Hashing and Random Projection Based Hashing. They are widely used for approximate nearest neighbor searching. These two algorithms treat all input features uniformly while they ignore feature importance and class separability. In this research we aim to propose a lightweight hashing framework named Adaptive Feature Aware Hashing which integrates feature weighting prior to projection-based hashing. The algorithm computes data-driven feature weights using variance, between-class separability, and Fisher-style discriminative criteria to enhance discriminative power during hash code generation. We also incorporated multi table and multi probe hashing which enhances discriminative power during hash code generation. For this research we used MNISH dataset for experimental evaluation. We compared the results against a Baseline Locality-Sensitive Hashing (LSH) method using random projections. Our results indicate that The AFAH methods (v1 and v2 Fisher) significantly improved both precision and recall compared to the Baseline LSH, with AFAH v2 Fisher showing the highest precision (0.7557) and AFAH v1 having the highest recall (0.2285).