A New Scheme for Image Recognition Using Higher-Order Local Autocorrelation and Factor Analysis

This paper proposes a new scheme for multipurpose image recognition based on Higher-order Local AutoCorrelation (HLAC) features and factor analysis. First, HLAC features, which are inherently invariant under translation, computationally inexpensive, and additive, are extracted from the input images. Second, factor analysis is applied to the feature vectors so as to decompose the feature vectors as combinations of factors leant through supervised training examples. After the factorization, the input image is recognized by using the factor scores obtained through the least squares method. Experimental results show that the proposed method effectively enables the system to recognize images by acquiring effective factors that represent each object in the images without any need for segmentation or locating objects.