FISH IMAGE ANALYSIS: FUSION OF MOMENT-BASED AND DIRECTIONAL FEATURES IN COLOUR SPACE

Abstract

This study introduces an innovative approach to content-based image retrieval (CBIR) specifically designed for fish species identification. The proposed method integrates shape, colour, and texture features using Zernike Moments Invariant (ZMI) and Local Directional Pattern (LDP), applied to the momentgram and the hue channel of the HSV colour space. This fusion ensures invariance to transformations such as rotation, scaling, and translation, enabling robust performance on natural images with varying orientations and quality. The method was evaluated using the Fish4Knowledge dataset, consisting of 27,370 images, with 30% randomly selected as query images. Experimental results demonstrate that the proposed method achieved a mean average precision (MAP) of 84.17%, significantly outperforming comparable state-of-the-art approaches. Statistical analysis using two-tailed paired t-tests confirms its superiority. By combining global shape descriptors, local texture features, and colour properties, this method delivers a comprehensive representation of fish images. The inclusion of moment-based descriptors enhances its robustness against low-resolution images and noise. This research underscores the importance of combining diverse features within CBIR systems and offers a significant improvement in retrieval accuracy, contributing to domain-specific applications such as sustainable fisheries management and aquaculture research.