SIGDOC '86 Proceedings of the 5th annual international conference on Systems documentation
P-Complete Approximation Problems
Journal of the ACM (JACM)
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Diogenes: a web search agent for person images
MULTIMEDIA '00 Proceedings of the eighth ACM international conference on Multimedia
Approximation algorithms
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
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
A Randomized Approximation Scheme for Metric MAX-CUT
FOCS '98 Proceedings of the 39th Annual Symposium on Foundations of Computer Science
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
Polynomial Time Approximation of Dense Weighted Instances of MAX-CUT (Revised Version)
Polynomial Time Approximation of Dense Weighted Instances of MAX-CUT (Revised Version)
Automatic Linguistic Indexing of Pictures by a Statistical Modeling Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Using syntactic dependency as local context to resolve word sense ambiguity
ACL '98 Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and Eighth Conference of the European Chapter of the Association for Computational Linguistics
Automatic multimedia cross-modal correlation discovery
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Regularizing translation models for better automatic image annotation
Proceedings of the thirteenth ACM international conference on Information and knowledge management
Formulating Semantic Image Annotation as a Supervised Learning Problem
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
A database centric view of semantic image annotation and retrieval
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Image annotations by combining multiple evidence & wordNet
Proceedings of the 13th annual ACM international conference on Multimedia
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
An adaptive graph model for automatic image annotation
MIR '06 Proceedings of the 8th ACM international workshop on Multimedia information retrieval
Image annotation refinement using random walk with restarts
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
The Google Similarity Distance
IEEE Transactions on Knowledge and Data Engineering
Proceedings of the 6th ACM international conference on Image and video retrieval
Refining image annotation using contextual relations between words
Proceedings of the 6th ACM international conference on Image and video retrieval
Using information content to evaluate semantic similarity in a taxonomy
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Extended gloss overlaps as a measure of semantic relatedness
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
CBSA: content-based soft annotation for multimodal image retrieval using Bayes point machines
IEEE Transactions on Circuits and Systems for Video Technology
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Recently, images on the Web and personal computers are prevalent around the human's life. To retrieve effectively these images, there are many (Automatic Image Annotation) AIA algorithms. However, it still suffers from low-level accuracy since it couldn't overcome the semantic-gap between low-level features (`color', `texture' and `shape') and high-level semantic meanings (e.g., `sky', `beach'). Namely, AIA techniques annotates images with many noisy keywords. In this paper, we propose a novel approach that augments the classical model with generic knowledge-based, WordNet. Our novel approach strives to prune irrelevant keywords by the usage of WordNet. To identify irrelevant keywords, we investigate various semantic similarity measures between keywords and finally fuse outcomes of all these measures together to make a final decision using Dempster-Shafer evidence combination. Furthermore, We can re-formulate the removal of erroneous keywords from image annotation problem into graph-partitioning problem, which is weighted MAX-CUT problem. It is possible that we have too many candidate keywords for web-images. Hence, we need to have deterministic polynomial time algorithm for MAX-CUT problem. We show that finding optimal solution for removing noisy keywords in the graph is NP-Complete problem and propose a new methodology for Knowledge Based Image Annotation Refinement (KBIAR) using a deterministic polynomial time algorithm, namely, randomized approximation graph algorithm. Finally, we demonstrate the superiority of this algorithm over traditional one including the most recent work for a benchmark dataset.