Image Classification for Trend Prediction Based on Integration of fNIRS and Visual Features

This paper presents a novel method of image classification for trend prediction based on integration of visual and fNIRS features. It is expected that classification of images in the same object category in terms of generation enables trend prediction. However, since images in the same object category have similar visual features, a limit of accuracy exists for image classification by using only visual features. To overcome this problem, we utilize fNIRS features which represent brain activity in addition to visual features. Specifically, we apply Discriminative Locality Preserving Canonical Correlation Analysis (DLPCCA) to fNIRS and visual features for utilizing them collaboratively. The main contribution of this paper is the improvement of classification performance of images in the same object category for trend prediction by using the visual features projected to the DLPCCA-based space.