Natural image classification driven by human brain activity

Dai Zhang; Hanyang Peng; Jinqiao Wang; Ming Tang; Rong Xue; Zhentao Zuo

doi:10.1117/12.2216752

29 March 2016 Natural image classification driven by human brain activity

Dai Zhang, Hanyang Peng, Jinqiao Wang, Ming Tang, Rong Xue, Zhentao Zuo

Proceedings Volume 9788, Medical Imaging 2016: Biomedical Applications in Molecular, Structural, and Functional Imaging; 97881L (2016) https://doi.org/10.1117/12.2216752
Event: SPIE Medical Imaging, 2016, San Diego, California, United States

Abstract

Natural image classification has been a hot topic in computer vision and pattern recognition research field. Since the performance of an image classification system can be improved by feature selection, many image feature selection methods have been developed. However, the existing supervised feature selection methods are typically driven by the class label information that are identical for different samples from the same class, ignoring with-in class image variability and therefore degrading the feature selection performance. In this study, we propose a novel feature selection method, driven by human brain activity signals collected using fMRI technique when human subjects were viewing natural images of different categories. The fMRI signals associated with subjects viewing different images encode the human perception of natural images, and therefore may capture image variability within- and cross- categories. We then select image features with the guidance of fMRI signals from brain regions with active response to image viewing. Particularly, bag of words features based on GIST descriptor are extracted from natural images for classification, and a sparse regression base feature selection method is adapted to select image features that can best predict fMRI signals. Finally, a classification model is built on the select image features to classify images without fMRI signals. The validation experiments for classifying images from 4 categories of two subjects have demonstrated that our method could achieve much better classification performance than the classifiers built on image feature selected by traditional feature selection methods.

Citation Download Citation

Dai Zhang, Hanyang Peng, Jinqiao Wang, Ming Tang, Rong Xue, and Zhentao Zuo "Natural image classification driven by human brain activity", Proc. SPIE 9788, Medical Imaging 2016: Biomedical Applications in Molecular, Structural, and Functional Imaging, 97881L (29 March 2016); https://doi.org/10.1117/12.2216752

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KEYWORDS

Brain

Feature selection

Image classification

Functional magnetic resonance imaging

Neuroimaging

Lab on a chip

Human subjects

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