The Comparison of Classifiers for Object Categorization Based on Bag-of-Word Technology

Object categorization has become active in the field of pattern recognition. There are two main factors which affect the performance of classification. One is the representation of images, and the other is the design of classifier. The representation of images based on bag-of-word (BOW) has become a...

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Bibliographic Details
Main Authors: Jian-Xin Huang, Yan-Yun Qu, Cui-Hua Li, Miao-Jun Hu
Format: Conference Proceeding
Language:chi ; eng
Subjects:
Computer science
Entropy
Image processing
Pattern recognition
Support vector machine classification
Support vector machines
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Summary:Object categorization has become active in the field of pattern recognition. There are two main factors which affect the performance of classification. One is the representation of images, and the other is the design of classifier. The representation of images based on bag-of-word (BOW) has become a popular method because of its simpleness and high efficiency. This paper aims to compare some state-of-the-art classifiers used in object categorization based on the BOW technology. In the dataset of Xerox7 and CalTech6, we compare the performance of five classifiers which are SVM, maximum entropy, naive Bayes, Adaboost and random forests. The result of experiments show that SVM and maximum entropy have better performance than others.
DOI:10.1109/CCPR.2009.5344138