TAG BASED IMAGE RETRIEVAL (TBIR) USING AUTOMATIC IMAGE ANNOTATION

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,...

Automatic Image Annotation for Semantic Image Retrieval

This paper addresses the challenge of automatic annotation of images for semantic image retrieval. In this research, we aim to identify visual features that are suitable for semantic annotation tasks. We propose an image classification system that combines MPEG-7 visual descriptors and support vector machines. The system is applied to annotate cityscape and landscape images. For this task, our ...

Automatic Image Annotation and Retrieval

In this paper, an automatic image annotation and retrieval model is developed base on the intensity invariant approach. The given uncaptioned image is divided into background and foreground images and segmented into regions, which are classified into region types using a variety of features. Firstly, preprocessing stages such as gray-scale converting, noise filtering for image enhancing is proc...

Optimal Tag Sets for Automatic Image Annotation

In this paper we introduce the Beam Search CRM (BS-CRM) model. This model implements two novel improvements to the basic CRM [2]. First, we argue that using a Minkowski kernel allows us to capture the covariance of visual features more effectively than the standard Gaussian kernel. Second, we advocate a procedure that selects the most informative subset of tags as the image annotation. Our proc...

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