Rotated complex wavelet transform with vocabulary tree for content based image retrieval

2010 ◽  
Author(s):  
Anil Balaji Gonde ◽  
R. P. Maheshwari ◽  
R. Balasubramanian
Keyword(s):  
Wavelet Transform ◽  
Image Retrieval ◽  
Content Based Image Retrieval ◽  
Complex Wavelet Transform ◽  
Vocabulary Tree ◽  
Complex Wavelet

Feature Set Reduction in Rotation Invariant CBIR Using Dual-Tree Complex Wavelet Transform

2012 ◽  
pp. 232-248
Author(s):  
Deepak Sharma ◽  
Ekta Walia ◽  
H.P. Sinha
Keyword(s):  
Fourier Transform ◽  
Wavelet Transform ◽  
Image Retrieval ◽  
Symmetry Property ◽  
Rotation Invariance ◽  
Image Feature ◽  
Content Based Image Retrieval ◽  
Complex Wavelet Transform ◽  
Complex Wavelets ◽  
Wavelets Transform

An accurate Content Based Image Retrieval (CBIR) system is essential for the correct retrieval of desired images from the underlying database. Rotation invariance is very important for accurate Content Based Image Retrieval (CBIR). In this chapter, rotation invariance in Content Based Image Retrieval (CBIR) system is achieved by extracting Fourier features from images on which Dual Tree Complex Wavelets Transform (DT-CWT) has been applied. Before applying DT-CWT, the Fourier feature set is reduced by exploiting the symmetry property of Fourier transform. For an N x N image, feature set has been reduced from N2/2 features to N2/4 features. This reduction in feature set increases the speed of the system. Hence, this chapter proposes a method which makes the Content Based Image Retrieval (CBIR) system faster without comprising accuracy and rotation invariance.

Color Image Retrieval Algorithm Based on Dual Tree Complex Wavelet Transform

2013 ◽  
Vol 427-429 ◽  
pp. 1761-1764
Author(s):  
Xiao Li Zhao ◽  
Shu Jun Yin
Keyword(s):  
Wavelet Transform ◽  
Image Retrieval ◽  
Color Image ◽  
Texture Features ◽  
Retrieval Algorithm ◽  
Complex Wavelet Transform ◽  
Invariant Moments ◽  
Frequency Components ◽  
Complex Wavelet ◽  
Form Feature

A color image retrieval algorithm was presented in order to retrieve image from massive images. This method firstly transformed image from RGB space to HSV space. V component of HSV space was decomposed into three levels by dual tree complex wavelet transform (DT-CWT) to extract high frequency components as texture features. Invariant moments of V component were extracted as shape features. Information of H component of HSV space was extracted as color features. Then three types of features were combined to form feature vector to judge similarity of images. Experiments show that this algorithm has not only high precision and recall but also low time consumption.

Sign in / Sign up

Export Citation Format

Share Document