Enhancing image retrieval accuracy through multi-resolution HSV-LNP feature fusion and modified K-NN relevance feedback

Content-Based Image Retrieval (CBIR) involves the use of algorithms designed to accurately perceive and retrieve images based on their visual content. This paper introduces the Multi-Resolution HSV-LNP Feature Fusion (MRHLFF) method which combines color and texture features. The color feature consists of three main steps starting with a Color Space Transformation (CST) step, which converts RGB images into the HSV color space, which is more suited to human color perception and robust under variable lighting conditions. The second step is HSV Histogram Computation (HHC) which involves creating normalized color histograms for Hue, Saturation, and Value channels in the HSV color space to represent color types, and brightness distribution across an image. Multi-Resolution Color Moment (MRCM) calculates six statistical moments at multiple resolutions for each HSV channel in the third step. Regarding the Texture feature, it also consists of three main steps including Grayscale Texture Preparation (GTP) which involves converting images to grayscale to focus on intensity variations in the first step. In the second step, Scale Normalization and Gradient Orientation (SNGO) standardize the size of grayscale images and analyze texture by calculating gradient orientations, generating a histogram of these orientations for consistent texture feature extraction. Finally, Rotation Invariant Texture Analysis (RITA) ensures uniform texture features by aligning images based on a dominant orientation and computing Local Neighbor Patterns (LNP) that are invariant to rotation, encapsulating detailed texture patterns into a histogram used in image analysis. Both Color and Texture features are combined into feature vectors by Feature Fusion Vector (FFV). Dimensionality Reduction via PCA (DRP) is used to enhance the data representation and retrieval efficiency. Finally, Relevance Feedback and Refinement (RF&R) uses user interactions to improve retrieval precision using the Modified k-NN Based Relevance Feedback Algorithm. This study evaluates the MRHLFF method across four standard databases: Corel 1k, Corel 10k, Brodatz, and Vistex, which shows its robustness and excellent performance in almost all cases.