An Efficient Image Retrieval Scheme for Sketches using Fish Swarm Optimization with the Aid of Optimal Score Level Fusion

Abstract

Objectives: Image retrieval is system software for browsing, examining and retrieving images from a large database of images. Images and sketches do not share numerous common modalities. Hence, Sketch to Image Retrieval is a tedious task in image processing. Sketch image retrieval focuses on the hand-drawn query and retrieves the similar images from a large database which is useful for further processing.Methods/Statistical Analysis:In the, most of the traditional/conventional image processing techniques considered edges and outlines for image retrieval. In this paper, a new methodology is developed by fusion of Edge Histogram Descriptors, Histogram of oriented gradients, Scale Invariant Feature Transform (SIFT) and Speeded up Robust Features (SURF). In the proposed model first feature Extraction is carried out and Euclidian distance is calculated amongst the query sketch and innovative image. Formerly the feature vectors are provided to the score level fusion stage, and then they obtained results are optimized. For optimization, Fish Swarm Optimization (FSO) is employed in the proposed method. Findings: The performance of the proposed method is evaluated through Benchmark sketch image database. Also, the attained results are compared with the existing evolutionary algorithm Genetic Algorithm (GA). Application/ Improvement: The experimental results showed that the projected method with FSO yields better results than GA.