Image ranking via attribute boosted hypergraph

Authors:
Zhou Yu;Siliang Tang;Yin Zhang;Jian Shao
Affiliations:
College of Computer Science, Zhejiang University, Hangzhou, P.R. China;College of Computer Science, Zhejiang University, Hangzhou, P.R. China;College of Computer Science, Zhejiang University, Hangzhou, P.R. China;College of Computer Science, Zhejiang University, Hangzhou, P.R. China
Venue:
PCM'12 Proceedings of the 13th Pacific-Rim conference on Advances in Multimedia Information Processing
Year:
2012

Citing 10
Cited 0

Multilevel hypergraph partitioning: applications in VLSI domain

IEEE Transactions on Very Large Scale Integration (VLSI) Systems
Graph partitioning models for parallel computing

Parallel Computing - Special issue on graph partioning and parallel computing
Distinctive Image Features from Scale-Invariant Keypoints

International Journal of Computer Vision
Higher order learning with graphs

ICML '06 Proceedings of the 23rd international conference on Machine learning
Ranking on graph data

ICML '06 Proceedings of the 23rd international conference on Machine learning
Representing shape with a spatial pyramid kernel

Proceedings of the 6th ACM international conference on Image and video retrieval
Music recommendation by unified hypergraph: combining social media information and music content

Proceedings of the international conference on Multimedia
Describable Visual Attributes for Face Verification and Image Search

IEEE Transactions on Pattern Analysis and Machine Intelligence
SURF: speeded up robust features

ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Baby talk: Understanding and generating simple image descriptions

CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition

Quantified Score

Hi-index	0.00

Visualization

Abstract

Recently, the visual attribute of images is becoming a research focus in computer vision and multimedia retrieval areas due to its describable or human-nameable nature for image understanding. In this paper, the visual attribute is utilized to boost the result of image ranking. To well modeling the images along with their visual attributes, hypergraph is used to integrate the visual attributes with low-level features of images. After that, we perform a ranking algorithm on the hypergraph. The experiment conducted on Animal with Attribute(AwA) dataset demonstrate the effectiveness of our proposed approach.