The semantic contextual information is shown to be an important resource for improving the scene and image recognition, but is seldom explored in the literature of previous distance metric learning (DML) for images. In this work, we present a novel Contextual Metric Learning (CML) method for learning a set of contextual distance metrics for real world multi-label images. The relationships betwe...

The demand for automatically annotating and retrieving medical images is growing faster than ever. In this paper, we present a novel medical image retrieval method for a special medical image retrieval problem where the images in the retrieval database can be annotated into one of the pre-defined labels. Even more, a user may query the database with an image that is close to but not exactly wha...

This paper proposed a method to find annotations corresponding to given CNN news documents for detecting terrorism image or context information. Assigning keywords or annotation to image is one of the important tasks to let machine understand web data written by human. Many techniques have been suggested for automatic image annotation in the last few years. Many researches focused on the method...

In this paper, we propose the use of the Maximum Entropy approach for the task of automatic image annotation. Given labeled training data, Maximum Entropy is a statistical technique which allows one to predict the probability of a label given test data. The techniques allow for relationships between features to be effectively captured. and has been successfully applied to a number of language t...

combine multiple cues, such as color, texture, shape, and layout, frequently without user interaction. These include We describe a system which automatically annotates images with a set of prespecified keywords, based on supervised color IBM’s Query-by-Image-Content (QBIC) [10], MIT’s Phoclassification of pixels into N prespecified classes using simple tobook [11], the Trademark and Art Museum ...

The paper discusses a region based approach for content based image retrieval. Due to the explosive growth of image databases there is a great need for new methods to handle large amount of data eeciently. Besides manual image annotation automatic feature extraction is necessary. It is desirable not only to compare whole images but also look up single objects in an image database.

In this digital world, artificial intelligence has provided solutions to many problems, likewise to encounter problems related to digital images and operations related to the extensive set of images. We should learn how to analyze an image, and for that, we need feature extraction of the content of that image. Image description methods involve natural language processing and concepts of compute...

Automatic image annotation refers to the process of automatically labeling an image with a predefined set of keywords. Image annotation is an important step of content-based image retrieval (CBIR), which is relevant for many real-world applications. In this paper, a new algorithm based on multiple grid segmentation, entropy-based information and a Bayesian classifier, is proposed for an efficie...

In order to exploit the massive image information and to handle overload, techniques for analyzing image content to facilitate indexing and retrieval of images have emerged. In this paper, a low-level and high-level image semantic annotation based on Fuzzy Petri Net is presented. Knowledge scheme is used to define more general and complex semantic concepts and their relations in the context of ...