Add to ORKGThis paper addresses the automatic analysis of visual content and extraction of metadata beyond pure visual descriptors. Two approaches are described: Automatic Image Annotation (AIA) and Confidence Clustering (CC). AIA attempts to automatically classify images based on two binary classifiers and is\ud designed for the consumer electronics domain. Contrastingly, the CC approach does not attempt to assign a unique label to images but rather to organise the database based on concepts
In this paper, it,e propose a novel strategy at an abstract level bv combining textual and visual cl...
Semantic spaces encode similarity relationships between objects as a function of position in a mathe...
The emergence of multimedia enabled devices, particularly the incorporation of cameras in mobile pho...
This paper addresses the automatic analysis of visual content and extraction of metadata beyond pure...
This paper addresses the challenge of automatic annotation of images for semantic image retrieval. I...
This paper describes an ongoing project which seeks to contribute to a wider understanding of the re...
Automatic image annotation enables efficient indexing and retrieval of the images in the large-scale...
This paper presents novel methods for automatically discovering, summarizing and evaluating multimed...
Digital multimedia content is omnipresent on the Web; Google posted on August 2005, a total image si...
This research work addresses the problem of using concept-related indexing of image content as a nea...
To address the semantic gap, state-of-the-art automatic image annotation frameworks concatenate the ...
Image clustering is an important technology which helps users to get hold of the large amount of onl...
In this paper, we introduce a novel high-level visual content descriptor which is devised for perfor...
Image data are omnipresent for various applications. A considerable volume of data is produced and w...
This paper adopts the premise that the 'semantic gap' is an incompletely surveyed feature in the lan...
In this paper, it,e propose a novel strategy at an abstract level bv combining textual and visual cl...
Semantic spaces encode similarity relationships between objects as a function of position in a mathe...
The emergence of multimedia enabled devices, particularly the incorporation of cameras in mobile pho...
This paper addresses the automatic analysis of visual content and extraction of metadata beyond pure...
This paper addresses the challenge of automatic annotation of images for semantic image retrieval. I...
This paper describes an ongoing project which seeks to contribute to a wider understanding of the re...
Automatic image annotation enables efficient indexing and retrieval of the images in the large-scale...
This paper presents novel methods for automatically discovering, summarizing and evaluating multimed...
Digital multimedia content is omnipresent on the Web; Google posted on August 2005, a total image si...
This research work addresses the problem of using concept-related indexing of image content as a nea...
To address the semantic gap, state-of-the-art automatic image annotation frameworks concatenate the ...
Image clustering is an important technology which helps users to get hold of the large amount of onl...
In this paper, we introduce a novel high-level visual content descriptor which is devised for perfor...
Image data are omnipresent for various applications. A considerable volume of data is produced and w...
This paper adopts the premise that the 'semantic gap' is an incompletely surveyed feature in the lan...
In this paper, it,e propose a novel strategy at an abstract level bv combining textual and visual cl...
Semantic spaces encode similarity relationships between objects as a function of position in a mathe...
The emergence of multimedia enabled devices, particularly the incorporation of cameras in mobile pho...