Feature generation for quantification of visual similarity
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Authors
Han, Tianning Steven
Issue Date
2014-05
Type
Electronic thesis
Thesis
Thesis
Language
ENG
Keywords
Computer science
Alternative Title
Abstract
The complex nature of visual similarity makes it extremely difficult to hand code a set of good features that incorporate all of the important aspects for all images. This thesis work shows that machine learning techniques can be used to generate statistically optimal low dimensional features that work well with calculating similarity using Euclidean distance between feature representation of images. Specifically, a Stacked Denoising Autoencoder (SDA) was used to train a deep neural network to learn a set of important features from the Amsterdam Library of Object Images. Theses features generated by SDA were compared with those generated using OBVIS, a feature generation algorithm developed specifically for human visual similarity comparison. The results indicated that features learned by SDA, a generic representation learning approach, outperformed the features generated by OBVIS, a method coded with domain specific knowledge.
Description
May 2014
School of Science
School of Science
Full Citation
Publisher
Rensselaer Polytechnic Institute, Troy, NY