Robust clustering for social annotated images

Authors:
Yang Wang;Jian Zheng;Xianglong Liu;Bo Lang
Affiliations:
National Computer Network Emergency Response Technical Team/Coordination Center of China, Beijing, China;Beihang University, Beijing, China;Beihang University, Beijing, China;Beihang University, Beijing, China
Venue:
Proceedings of the Fifth International Conference on Internet Multimedia Computing and Service
Year:
2013

Citing 17
Cited 0

Normalized Cuts and Image Segmentation

IEEE Transactions on Pattern Analysis and Machine Intelligence
Why so many clustering algorithms: a position paper

ACM SIGKDD Explorations Newsletter
An Optimal Graph Theoretic Approach to Data Clustering: Theory and Its Application to Image Segmentation

IEEE Transactions on Pattern Analysis and Machine Intelligence
Content-based image retrieval by clustering

MIR '03 Proceedings of the 5th ACM SIGMM international workshop on Multimedia information retrieval
A probabilistic framework for semi-supervised clustering

Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Supervised Clustering " Algorithms and Benefits

ICTAI '04 Proceedings of the 16th IEEE International Conference on Tools with Artificial Intelligence
Introduction to Data Mining, (First Edition)

Introduction to Data Mining, (First Edition)
Web image clustering by consistent utilization of visual features and surrounding texts

Proceedings of the 13th annual ACM international conference on Multimedia
New indices for cluster validity assessment

Pattern Recognition Letters
A tutorial on spectral clustering

Statistics and Computing
Network properties of folksonomies

AI Communications - Network Analysis in Natural Sciences and Engineering
Exploring social annotations for information retrieval

Proceedings of the 17th international conference on World Wide Web
Web search personalization via social bookmarking and tagging

ISWC'07/ASWC'07 Proceedings of the 6th international The semantic web and 2nd Asian conference on Asian semantic web conference
Survey on social tagging techniques

ACM SIGKDD Explorations Newsletter
Least squares quantization in PCM

IEEE Transactions on Information Theory
New spectral methods for ratio cut partitioning and clustering

IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Compact hashing for mixed image-keyword query over multi-label images

Proceedings of the 2nd ACM International Conference on Multimedia Retrieval

Quantified Score

Hi-index	0.00

Visualization

Abstract

Clustering plays an important role in large-scale image data management. The supervised clustering methods can discover the meaningful data groups according to the image labels. However, social annotated labels usually contain lots of noises like semantic ambiguity and redundancy, and thus the accuracy of clustering cannot be guaranteed. This paper firstly analyzes the characteristics of social annotated labels: label co-occurrence, ambiguity and redundancy, and then proposes a new image clustering method using semantic labels and their co-occurrence statistics and designs the similarity metric for social annotated images. Moreover, this paper presents a dynamic and efficient thresholding scheme for adaptively terminate the spectral clustering process. Finally, a social annotated image data set is constructed for algorithm evaluation. In our experiments, we compared our method with classic ones, and the results show that our method has better robustness and efficiency on social annotated images in terms of both accuracy and balance.