An Efficient Method for Appearance-based Object Recognition
Date Issued
2007
Date
2007
Author(s)
Chang, Wei-Hao
DOI
en-US
Abstract
In computer vision, the ways to make computers being capable of seeing and understanding the world have been intensely studied more than three decades. In this thesis, an efficient method for appearance-based object recognition is proposed. The proposed method including both the training phase and recognition phase is very efficient.
The concept of image retrieval is applied in our method. It tries to find the most similar feature vector set in image databases and classify the testing image by using the most frequent class label in the feature vector set.
For accomplishing the proposed object recognition method, we developed several techniques: an efficient pattern rejection scheme - Hierarchical Rejection Tree is first proposed here. Hierarchical rejection tree can find the most similar feature vector in database efficiently. Based on Hel-Or’s Projection Scheme [3], we enhanced its performance by iterative indexing tree structure. The experimental result shows the performance of Hierarchical Rejection Tree is faster 2.6 times than projection scheme. In this thesis, two new effective feature extraction methods are also be introduced. For an image, the hue mean/intensity DCT-WH image feature captures color information and DCT spectrum as its characteristics. For an image, the other feature extraction, the hue mean/hue histogram-WH image feature captures color information and color distribution as its characteristics.
We combine the Hierarchical Rejection Tree technique with feature extraction methods to develop our appearance-based object recognition system. Three image databases which contain large number of object images are used as testing databases. The experimental results show the proposed method has the following features: fast training speed, fast recognition speed and high recognition rate.
The concept of image retrieval is applied in our method. It tries to find the most similar feature vector set in image databases and classify the testing image by using the most frequent class label in the feature vector set.
For accomplishing the proposed object recognition method, we developed several techniques: an efficient pattern rejection scheme - Hierarchical Rejection Tree is first proposed here. Hierarchical rejection tree can find the most similar feature vector in database efficiently. Based on Hel-Or’s Projection Scheme [3], we enhanced its performance by iterative indexing tree structure. The experimental result shows the performance of Hierarchical Rejection Tree is faster 2.6 times than projection scheme. In this thesis, two new effective feature extraction methods are also be introduced. For an image, the hue mean/intensity DCT-WH image feature captures color information and DCT spectrum as its characteristics. For an image, the other feature extraction, the hue mean/hue histogram-WH image feature captures color information and color distribution as its characteristics.
We combine the Hierarchical Rejection Tree technique with feature extraction methods to develop our appearance-based object recognition system. Three image databases which contain large number of object images are used as testing databases. The experimental results show the proposed method has the following features: fast training speed, fast recognition speed and high recognition rate.
Subjects
外觀
物件辨識
模式剃除
特徵抽取
模式匹配
appearance
object recognition
pattern rejection
feature extraction
pattern matching
Walsh-Hadamard
Type
thesis
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