Hierarchical Image Representation for General Object Categorization
Date Issued
2007
Date
2007
Author(s)
Lu, Wang-Chou
DOI
en-US
Abstract
Humans and animals classify the general objects in daily l ife. After classifying the objects, they use the prior knowledge of this object class to do better decisions and actions. We hope one day the machines are also capable of this task. There are two directions in solving this problem. One is generating a more representative representation from images. Another is using better machine learning algorithms on the generated representation. This thesis walks toward the former direction.
After surveying previous work, we discover a group of common operations in making a better representation. Based on these observations, a new feed -forward hierarchical
model for image representation is proposed. This new model has the characteristi c of having large plasticity. The new model can fit the requirement of object classes by
generating different descriptors.
We build a simple 4-layers hierarchical image Categorization system to test this new model. This system is evaluated on the PASCAL Visual Object Challenge 2006 data set.
And we get a similar performance of Bag of Words model.
Subjects
影像辨識
分類
Image Categorization
Image Classification
Type
thesis
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