Image Retrieval Systems

In these systems there is a tradeoff between accuracy and computational cost. This tradeoff is mitigated by more efficient algorithms and improved computing power to make it cheaper. In addition, existing systems utilize small to medium image databases to produce effective results, but are generally not suitable when applied to large size image databases. Research designed and proposed techniques to improve the image search process of large scale databases from the viewpoint of accuracy and speed.

An image retrieval system is a computer system for viewing, retrieving and retrieving images from a large digital image database. Powerful natural, geographical and medical image retrieval using supervised classifiers focused on extracted features has been proposed. - Segmentation of images The purpose of segmentation of background images is to segment the image into multiple segments. This is useful because it simplifies or modifies the image to make it easier to split and make it more meaningful. (Wikipedia, 2013) Segmentation is based on various measurements taken from images. This is often the first step to fully understanding the image. Image segmentation is based on measurements taken from images, such as grayscale, color, texture, motion

Image annotation is the process of automatically assigning titles or keywords to digital images. Application to image search system for searching images in database was found. Machine learning methods and algorithms apply to automatic image annotation. Clustering and classification is the most common method used in image annotation. Reinforcement learning is a machine learning algorithm that learns how an agent interacts with the environment and behaves in the environment. In the field of machine learning, much research has been done recently. It is mainly used in games and robotics engineering. This algorithm approach is different from other machine learning algorithms, supervised learning and unsupervised learning.