A HUMAN-CENTERED NEURAL NETWORK MODEL WITH DISCRIMINATIVE LOCALITY PRESERVING CANONICAL CORRELATION ANALYSIS FOR IMAGE CLASSIFICATION

摘要

This paper presents a human-centered neural network model with discriminative locality preserving canonical correlation analysis (DLPCCA) for image classification. Although construction of multiple hidden layers adopted in recent deep learning methods is effective for extracting semantic features, a large amount of training images is required. In order to extract effective features for image classification successfully from a small amount of training images, the proposed method transforms visual features by using biological information obtained from image viewers as auxiliary information. The proposed method consists of two hidden layers. By constructing the first hidden layer, which can maximize canonical correlation between visual features and features based on biological information, the effective feature transformation can be realized. Specifically, the proposed method uses DLPCCA, which considers label information and preserves local structures. The second hidden layer constructed based on Extreme Learning Machine (ELM) enables classification. Consequently, since the first hidden layer performs the effective feature transformation, the proposed neural network model realizes accurate image classification from a quite small amount of training images.