Knowledge Based Image Annotation Refinement

Fuzzy Knowledge-based Image Annotation Refinement

Automatic image annotation methods automatically assign labels to images in order to facilitate tasks such as image retrieval. Incorrect labels may negatively influence the search results so image annotation should be as accurate as possible. Labels pertaining to objects or to whole scenes are commonly used for image annotation, and precision is especially important in case when scene labels ar...

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

A knowledge-based multi-layered image annotation system

Major challenge in automatic image annotation is bridging the semantic gap between the computable lowlevel image features and the human-like interpretation of images. The interpretation includes concepts on different levels of abstraction that cannot be simply mapped to features but require additional reasoning with general and domain-specific knowledge. The problem is even more complex since k...

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,...

Incentive Based Image Annotation

In this paper, we present a novel annotation paradigm with an emphasis on two facets – (a) semantic propagation and (b) an end user experience that provides insight. We attempt to propagate semantics of the annotations, by using WordNet, and low-level features extracted from the images. We introduce novel semantic dissimilarity measures, and propagation frameworks. The system also provides insi...