A graph-based image annotation framework

Scalable Image Annotation by Summarizing Training Samples into Labeled Prototypes

By increasing the number of images, it is essential to provide fast search methods and intelligent filtering of images. To handle images in large datasets, some relevant tags are assigned to each image to for describing its content. Automatic Image Annotation (AIA) aims to automatically assign a group of keywords to an image based on visual content of the image. AIA frameworks have two main sta...

Tags Re-ranking Using Multi-level Features in Automatic Image Annotation

Automatic image annotation is a process in which computer systems automatically assign the textual tags related with visual content to a query image. In most cases, inappropriate tags generated by the users as well as the images without any tags among the challenges available in this field have a negative effect on the query's result. In this paper, a new method is presented for automatic image...

Semantic-Based Image Retrial in the VQ Compressed Domain using Image Annotation Statistical Models

Fuzzy Neighbor Voting for Automatic Image Annotation

With quick development of digital images and the availability of imaging tools, massive amounts of images are created. Therefore, efficient management and suitable retrieval, especially by computers, is one of themost challenging fields in image processing. Automatic image annotation (AIA) or refers to attaching words, keywords or comments to an image or to a selected part of it. In this paper,...

A CAD System Framework for the Automatic Diagnosis and Annotation of Histological and Bone Marrow Images

Due to ever increasing of medical images data in the world’s medical centers and recent developments in hardware and technology of medical imaging, necessity of medical data software analysis is needed. Equipping medical science with intelligent tools in diagnosis and treatment of illnesses has resulted in reduction of physicians’ errors and physical and financial damages. In this article we pr...