1 2 1

1. 100081;

E_mail:qingnie@bit.edu.cn

2. 100081

:

Image Classification Technology Based on Mining of Frequent Item sets

Qing Nie1, Shou-yi Zhan

2, Jing-xia Su

1

1. School of Information Science and Technology, Beijing Institute of Technology, Beijing 100081, China;

E_mail:qingnie@bit.edu.cn

2. School of Computer Science, Beijing Institute of Technology, Beijing 100081,China.

Abstract We propose a novel method to detect frequent and distinctive feature configuration on a class instance. Each

neighborhood of a local feature is described by a list of nonzero indices, and generates a transaction. An efficient mining of frequent

item sets algorithm is used to automatically find spatial configurations of local features occurring frequently on a class instance.

These mined spatial configurations can be used as special words, incorporate into bag of features classification model. Through

evaluation on PASCAL 2007 Visual Recognition Challenge dataset set, the test results show that this mining algorithm is

computationally efficient and allows to process large training sets rapidly. Moreover, the mined feature configurations have higher

discriminative power compare to individual features.

Key Words: Frequent item sets, Image classification, Image recognition, Object Recognition

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