Visual attention based bag-of-words model for image classification

Qiwei Wang; Shouhong Wan; Lihua Yue; Che Wang

doi:10.1117/12.2064432

16 April 2014 Visual attention based bag-of-words model for image classification

Qiwei Wang, Shouhong Wan, Lihua Yue, Che Wang

Proceedings Volume 9159, Sixth International Conference on Digital Image Processing (ICDIP 2014); 91591P (2014) https://doi.org/10.1117/12.2064432
Event: Sixth International Conference on Digital Image Processing, 2014, Athens, Greece

Abstract

Bag-of-words is a classical method for image classification. The core problem is how to count the frequency of the visual words and what visual words to select. In this paper, we propose a visual attention based bag-of-words model (VABOW model) for image classification task. The VABOW model utilizes visual attention method to generate a saliency map, and uses the saliency map as a weighted matrix to instruct the statistic process for the frequency of the visual words. On the other hand, the VABOW model combines shape, color and texture cues and uses L1 regularization logistic regression method to select the most relevant and most efficient features. We compare our approach with traditional bag-of-words based method on two datasets, and the result shows that our VABOW model outperforms the state-of-the-art method for image classification.

Citation Download Citation

Qiwei Wang, Shouhong Wan, Lihua Yue, and Che Wang "Visual attention based bag-of-words model for image classification", Proc. SPIE 9159, Sixth International Conference on Digital Image Processing (ICDIP 2014), 91591P (16 April 2014); https://doi.org/10.1117/12.2064432

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