A Catalog of 1-D Features in Natural Images

Abstract

This paper explores the local form of actual feature types contained in real images. The local energy feature detector is used to locate points in an image where features are found. An unsupervised neural network is trained to capture the mean luminance values and standard deviations of the luminance values in a small neighborhood of these feature points. This local luminance information is called a feature template. After culling and normalization, we arrive at a catalog of local feature forms for the image. Our experiments indicate that the feature forms are self-similar over different images and across scales. When described by their phase angle, features also show some clustering around a small number of types. The size of the feature catalog is small, and shows promising applications in the area of image compression and reconstruction. Quantization of phase angles around the central angles of clusters yields a catalog of synthetic feature templates that further improves the fidelity of the reconstructed images.