Classification and Retrieval of Images Based on Extensive Context and Content Feature Set

摘要

Background: This paper renders a classification and retrieval of image achievements inthe search area of image retrieval, especially content-based image retrieval, an area that has beenvery active and successful in the past few years.Objective: Primarily the features extracted established on the bag of visual words (BOW) can be arrangedby utilizing Scaling Invariant Feature Transform (SIFT) and developed K-Means clusteringmethod.Methods: The texture is extracted for a developed multi-texton method by our study. Our retrievalprocess consists of two stages such as retrieval and classification. The images will be classified establishedon the features by applying k- Nearest Neighbor (kNN) algorithm. This will separate theimages into various classes in order to develop the precision and recall rate initially.Results: After the classification of images, the similar images are retrieved from the relevant class asper the afforded query image.