Content Based Image Retrieval using Multi-level 3D Color Texture and Low Level Color Features with Neural Network Based Classification System

Abstract

Content based image retrieval (CBIR), is an application of real-world computer vision domain where from a query image, similar images are searched from the database. The research presented in this paper aims to find out best features and classification model for optimum results for CBIR system.Five different set of feature combinations in two different color domains (i.e., RGB & HSV) are compared and evaluated using Neural Network Classifier, where best results obtained are 88.2% in terms of classifier accuracy. Color moments feature used comprises of: Mean, Standard Deviation,Kurtosis and Skewness. Histogram features is calculated via 10 probability bins. Wang-1k dataset is used to evaluate the CBIR system performance for image retrieval.Research concludes that integrated multi-level 3D color-texture feature yields most accurate results and also performs better in comparison to individually computed color and texture features.

Research on Visual Image Texture Rendering for Artistic Aided Design

“…To realize the twodimensional texture recognition of color images based on computer vision, first, we build a color multitexture image acquisition model [15], use the local window feature detection method to extract the contour feature points Q and P of the color multitexture image, and combine the correlation. According to the fusion rule [16], the maximum value pixel A of the two-dimensional edge pixel feature components of the color multitexture image is Scientific Programming…”