Spiking neural network-based edge detection model for content-based image retrieval

Content-based image retrieval (CBIR) techniques are widely used for extracting specific images from large databases. Recent studies have shown that edge features, alongside colors, align closely with human perception in CBIR. However, most CBIR approaches detect edges using linear methods like gradients, which do not align with how the human visual system (HVS) perceives edges. Bioinspired approaches, based on HVS, have proven more effective for edge detection. This study introduces a novel bioinspired spiking neural network (SNN)-based edge detection method for CBIR. The proposed method reduces computational costs by approximately 2.5 times compared to existing SNN models and offers a simpler, easily integrated structure. When integrated into CBIR techniques using conventional edge detection methods (Sobel, Canny, and image derivatives), it increased the mean precision on the Corel-1k dataset by over 3%. These results indicate that the proposed method is effective for edge-based CBIR.