Abstract

Background: This paper renders a classification and retrieval of image achievements in the search area of image retrieval, especially content-based image retrieval, an area that has been very active and successful in the past few years.

Objective: Primarily the features extracted established on the bag of visual words (BOW) can be arranged by utilizing Scaling Invariant Feature Transform (SIFT) and developed K-Means clustering method.

Methods: The texture is extracted for a developed multi-texton method by our study. Our retrieval process consists of two stages such as retrieval and classification. The images will be classified established on the features by applying k- Nearest Neighbor (kNN) algorithm. This will separate the images 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 as per the afforded query image.

Keywords: Invariant feature transform, k-means clustering, multi texton, k-nearest neighbor, precision, recall, retrieval.