Multiple Resolution Image Classification

Binary image classifiction is a problem that has received much attention in recent years. In this paper we evaluate a selection of popular techniques in an effort to find a feature set/ classifier combination which generalizes well to full resolution image data. We then apply that system to images at one-half through one-sixteenth resolution, and consider the corresponding error rates. In addition, we further observe generalization performance as it depends on the number of training images, and lastly, compare the system's best error rates to that of a human performing an identical classification task given teh same set of test images

Visual object concept discovery: Observations in congenitally blind children, and a computational approach

Over the course of the first few months of life, our brains accomplish a remarkable feat. They are able to interpret complex visual images so that instead of being just disconnected collections of colors and textures, they become meaningful sets of distinct objects. Exactly how this is accomplished is poorly understood. We approach this problem from both experimental and computational perspectives. On the experimental side, we have launched a new humanitarian and scientific initiative in India, called ‘Project Prakash’. This project involves a systematic study of the development of object-perception skills in children following recovery from congenital blindness. Here, we provide an overview of Project Prakash and also describe a specific study related to the development of faceperception skills following sight recovery. Based in part on the results of these experiments, we then develop a computational framework for addressing the problem of object concept discovery. Our model seeks to find repeated instances of a pattern in multiple training images. The source of complexity lies in the non-normalized nature of the inputs: the pattern is unconstrained in terms of where it can appear in the images, the background is complex and constitutes the overwhelming majority of the image, and the pattern can change significantly from one training instance to another. For the purpose of demonstration, we focus on human faces as the pattern of interest, and describe the sequence of steps through which the model is able to extract a face concept from non-normalized example images. Additionally, we test the model’s robustness to degradations in the inputs. This is important to assess the model’s congruence with developmental processes in human infancy, or following treatment for extended congenital blindness, when visual acuity is significantly compromised. r 2007 Published by Elsevier B.V