Accession Number:

ADA592782

Title:

Learning Distance Functions for Exemplar-Based Object Recognition

Descriptive Note:

Doctoral thesis

Corporate Author:

CALIFORNIA UNIV BERKELEY DEPT OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCE

Personal Author(s):

Report Date:

2007-01-01

Pagination or Media Count:

155.0

Abstract:

This thesis investigates an exemplar-based approach to object recognition that learns, on an image-by-image basis, the relative importance of patch-based features for determining similarity. We borrow the idea of family resemblances from Wittgensteins Philosophical Investigations and Eleanor Roschs psychological studies to support the idea of learning the detailed relationships between images of the same category which is a departure from some popular machine learning approaches such as Support Vector Machines that seek only the boundaries between categories. We represent images as sets of patch-based features. To find the distance between two images, we first find for each patch its nearest patch in the other image and compute their inter-patch distance. The weighted sum of these inter-patch distances is defined to be the distance between the two images. The main contribution of this thesis is a method for learning a set-to-set distance function specific to each training image and demonstrating the use of these functions for image browsing, retrieval and classification. The goal of the learning algorithm is to assign a non-negative weight to each patch-based feature of the image such that the most useful patches are assigned large weights and irrelevant or confounding patches are given zero weights. We formulate this as a large-margin optimization, related to the soft-margin Support Vector Machine, and discuss two versions a focal version that learns weights for each image separately, and a global version that jointly learns the weights for all training images.

Subject Categories:

  • Psychology
  • Optics

Distribution Statement:

APPROVED FOR PUBLIC RELEASE