Hidden Annotation in Content Based Image

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

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...

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,...

An Efficient Query Mining Framework Using Spatial Hidden Markov Models for Automatic Annotation of Images

A novel method for automatic annotation of images is used with keywords from a generic vocabulary of concepts or objects combined with annotation-based retrieval of images. This can be done by using spatial hidden Markov model, in which states represent concepts. The parameters of this model are estimated from a set of manually annotated training images. An image in a large test collection is t...

A bipartite graph model for associating images and text

The joint modeling of image and textual content is even more important now because of the the availability of large databases of image-rich web pages and the tagging phenomenon. Much of the current work focused on one-way association (image to text or tags). The association is often captured by building a model with hidden variables. In this paper, we propose a simple model based on random walk...