Content-based diversifying leaf image retrieval

Authors:
Sheng-Ping Zhu;Ji-Xiang Du;Chuan-Min Zhai
Affiliations:
Department of Computer Science and Technology, Huaqiao University, Xiamen, China;Department of Computer Science and Technology, Huaqiao University, Xiamen, China;Department of Computer Science and Technology, Huaqiao University, Xiamen, China
Venue:
ICIC'13 Proceedings of the 9th international conference on Intelligent Computing Theories and Technology
Year:
2013

Citing 8
Cited 0

The use of MMR, diversity-based reranking for reordering documents and producing summaries

Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Content-Based Image Retrieval at the End of the Early Years

IEEE Transactions on Pattern Analysis and Machine Intelligence
Support vector machine active learning for image retrieval

MULTIMEDIA '01 Proceedings of the ninth ACM international conference on Multimedia
Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope

International Journal of Computer Vision
Leaf Image Retrieval with Shape Features

VISUAL '00 Proceedings of the 4th International Conference on Advances in Visual Information Systems
Representing shape with a spatial pyramid kernel

Proceedings of the 6th ACM international conference on Image and video retrieval
Diversifying Image Retrieval with Affinity-Propagation Clustering on Visual Manifolds

IEEE MultiMedia
LIBSVM: A library for support vector machines

ACM Transactions on Intelligent Systems and Technology (TIST)

Quantified Score

Hi-index	0.00

Visualization

Abstract

In recent years, content-based image retrieval achieved continuous development, the main goal so far has been to retrieve similar objects for a given query, and only the relevance is cared in retrieval system, so many duplicate or near duplicate documents retrieved in response to a query. For efficient content-based image retrieval, we propose the Content-based Diversifying Leaf Image Retrieval in this paper. In order to make the retrieval results have relevance and diversity, we extract leaf image feature and use the relevance feedback technique based of SVM and the AP clustering algorithm. We also proposed a new evaluation function - Maximal Scatter Diversity (MSD) static evaluation function. Experimental results show that our approach can achieve good performance with improving the diversity of the retrieval results without reduction of their relevance.