A Machine Learning Model for Content-Based Image Retrieval

Abstract

With advancement in the modern era, digital data is the new asset. Now making large image datasets is not that laborious task with improvement in image collection and data storage technology. Image processing is a method to perform some operations in order to enhance an image or to extract some useful information from it. It is a type of single processing in which input is an image and output may be image or characteristics/features associated with that image. Nowadays image processing is a rapidly growing technology in the computer science field. It forms a core research area within engineering and computer science disciplines too. In the last one decade, we have witnessed various theories and models related to image retrieval from databases. Content based image retrieval is one of the frameworks image retrievals, having the advantage of reducing ambiguity related to textual indexation and less time consuming. It scans the database to find the most relative images according to the query. In this paper, we have worked on a model that retrieves images based on feature sets and displays the resultant images with minimum vector distance.