Entropy based image semantic cycle for image classification

Authors:
Hongyu Li;Junyu Niu;Lin Zhang
Affiliations:
Electronic Engineering Department, Fudan University, Shanghai, China,School of Software Engineering, Tongji University, Shanghai, China;School of Computer Science, Fudan University, Shanghai, China;School of Software Engineering, Tongji University, Shanghai, China
Venue:
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part V
Year:
2012

Citing 5
Cited 0

SVM binary classifier ensembles for image classification

Proceedings of the tenth international conference on Information and knowledge management
Video Google: A Text Retrieval Approach to Object Matching in Videos

ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Learning generative visual models from few training examples: An incremental Bayesian approach tested on 101 object categories

Computer Vision and Image Understanding
Representing shape with a spatial pyramid kernel

Proceedings of the 6th ACM international conference on Image and video retrieval
Fast active tabu search and its application to image retrieval

IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper proposes a novel framework for image classification with an entropy based image semantic cycle. Entropy minimization leads to an optimal image semantic cycle where images are connected in the semantic order. For classification, the training step is to find an optimal image semantic cycle in an image database. In the test step, the suitable position of an unknown image in this cycle is first found. Then, the class membership is determined through recognizing the nearest neighbors at this position. Experimental results demonstrate that the proposed framework achieves higher classification accuracy.