Paper Info
Title
Feature Annotation for Visual Concept Detection in ImageCLEF 2008.
Abstract
This paper shows our work on CLEF 2008. Our group joined the Visual Concept Detection Task of ImageCLEF 2008 this year. We submitted one run (run id: HJ_FA) for the evaluation. In the run, we applied a method called "Feature Annotation" to detect visual concept for the predefined concepts and we want to know how this information help in solving the photographic retrieval task. The applied method selected high level features for each concept from both local and global features, based on which the visual concepts are detected. The applied method consists of three procedures. First, feature extraction in which both local and global features are extracted from images. Then, a clustering algorithm is applied to "annotate the features". In this procedure, the features are affiliated with their corresponding concepts. Finally, we applied KNN algorithm to classify tests images according to the training images with the annotated features. The experiments were performed on the given training and test data on the 17 concepts. The paper concludes with an analysis of our results. Finally we identify the weaknesses in our approach and ways in which the algorithm could be optimized and improved.
Year
Venue
Keywords
2008
CLEF (Working Notes)
image retrieval,image classification,feature extraction
Field
DocType
Citations 
k-nearest neighbors algorithm,Annotation,Pattern recognition,Computer science,Feature extraction,Test data,Artificial intelligence,Cluster analysis,Clef
Conference
0
PageRank 
References 
Authors
0.34
3
3
Name
Order
Citations
PageRank
Jingtian Jiang1101.68
Xiaoguang Rui2877.59
Nenghai Yu32238183.33