Object Recognition Using Global and Local Features
Date Issued
2006
Date
2006
Author(s)
Lan, Hsiang-Ting
DOI
en-US
Abstract
The goal of object recognition is to identify the object in an image. In this thesis, we proposed a data mining approach to realize object recognition. Our proposed method consists of three phases. In the preprocessing phase, we normalize the image to make our method invariant to translation, scale and rotation. We do Haar discrete wavelet transform on the coordinates of the extracted contour and get 32 coefficients to be the global feature. Then we use each contour point to be the centroid of a window, and calculate the angle histogram to get some vectors as local features. In the training phase, we use these global and local features to find the patterns of each class. In the testing phase, some images are used to test the effectiveness of the representative patterns. For each test image, we calculate the ratios of the global and local patterns of the test image conformed to each class, and use a weight to combine both ratios. Finally, the image is classified into the class with the highest ratio. The experimental results show that the classification accuracy rate of our method achieves 98.15% in the leaves database and 97.62% in the ETH object database and outperforms the method proposed by Sun and Super.
Subjects
物件辨識
特徵樣式
小波轉換
角度直方圖
object recognition
feature pattern
wavelet transform
angle histogram
Type
other
File(s)![Thumbnail Image]()
Loading...
Name
ntu-95-R93725015-1.pdf
Size
23.31 KB
Format
Adobe PDF
Checksum
(MD5):2b7df770e565d8918637ce4897763f4a