Automatic image annotation based on relevance feedback

Authors:
Ke Chen;Ye Liang;Ying-Hong Liang;Jin-Xiang Li
Affiliations:
JiangSu Province Support Software Engineering, R&D Center for Modern Information Technology Application in Enterprise, Suzhou, China,Suzhou Vocational University, Suzhou, China;Suzhou Vocational University, Suzhou, China;Suzhou Vocational University, Suzhou, China;Suzhou Vocational University, Suzhou, China
Venue:
ICIC'12 Proceedings of the 8th international conference on Intelligent Computing Theories and Applications
Year:
2012

Citing 4
Cited 0

Object Recognition as Machine Translation: Learning a Lexicon for a Fixed Image Vocabulary

ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
Automatic image annotation and retrieval using cross-media relevance models

Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Automatic Linguistic Indexing of Pictures by a Statistical Modeling Approach

IEEE Transactions on Pattern Analysis and Machine Intelligence
On image auto-annotation with latent space models

MULTIMEDIA '03 Proceedings of the eleventh ACM international conference on Multimedia

Quantified Score

Hi-index	0.00

Visualization

Abstract

An image automatic annotation algorithm based on relevance feedback is proposed. Firstly, images are segmented into regions, and then the regions can generate blobs according to image features using clustering. Given a training set of images with annotations, we can compute the probability of a word given the image regions so as to automatically generate keywords for un-annotated image. Considering correlations among different semantics concepts, we employ condition probability to present two types of connections among different semantics concepts, and use the user's feedback information to adjust the probabilities of the keywords in annotation. The test results with Ground Truth Database illustrate the effect and efficiency of this algorithm.