DISTRIBUTED VARIANCE IN LOCALIZED PRINCIPAL COMPONENTS OF WHITENED NATURAL SCENES

Abstract

Naïve models of V1 simple cells, based on principal components analysis (PCA), have poor performance and do not form a distributed population code, limiting their scientific value and practical application in pattern recognition of natural scenes. This paper evaluates two strategies for enhancing the validity of PCA models that have been applied to comparable nonorthogonal models: pre-cortical "whitening", and local decomposition of visual features. The goal is to form a distributed population code. Four sets (landscapes, trees and plants, people and animals, and books and buildings) of sixteen natural images (256 × 256 pixels) were whitened and decomposed into 160 randomly selected local segments of either 8 × 8 or 16 × 16 pixels. The variance distribution of each representation was evaluated using a "distributed coding efficiency index" (DCE). Highly significant increases in DCE were observed for the principal components of local, whitened image segments compared to whole, nonwhitened images. This suggests that whitening and local decomposition may improve PCA performance for natural image processing, potentially providing more accurate simple cell models. In addition, the comparatively good performance of more complex, nonorthogonal models may be partially explained by their use of whitening and local decomposition.