Image retrieval using scale-space matching

1996 ◽  
pp. 273-282 ◽  
Author(s):  
S. Ravela ◽  
R. Manmatha ◽  
E. M. Riseman
Keyword(s):  
Image Retrieval ◽  
Scale Space

THE NORMALIZED WAVELET DESCRIPTOR FOR SHAPE RETRIEVAL

2008 ◽  
Vol 06 (01) ◽  
pp. 25-36 ◽  
Author(s):  
KIMCHENG KITH ◽  
BAREND J. VAN WYK ◽  
MICHAËL A. VAN WYK
Keyword(s):  
Image Analysis ◽  
Image Retrieval ◽  
Shape Descriptor ◽  
Scale Space ◽  
Experimental Results ◽  
Shape Retrieval ◽  
Fourier Descriptor ◽  
Curvature Scale Space ◽  
Wavelet Descriptor

In many image analysis applications, such as image retrieval, the shape of an object is of primary importance. In this paper, a new shape descriptor, namely the Normalized Wavelet Descriptor (NWD), which is a generalization and extension of the Wavelet Descriptor (WD), is introduced. The NWD is compared to the Fourier Descriptor (FD), which in image retrieval experiments conducted by Zhang and Lu, outperformed even the Curvature Scale Space Descriptor (CSSD). Image retrieval experiments have been conducted using a dataset containing 2D-contours of 1400 objects extracted from the standard MPEG7 database. For the chosen dataset, our experimental results show that the NWD outperforms the FD.

Trademark Image Retrieval

2011 ◽  
pp. 82-114
Author(s):  
Wing-Yin Chau ◽  
Chia-Hung Wei ◽  
Yue Li
Keyword(s):  
Image Retrieval ◽  
Scale Space ◽  
Zernike Moments ◽  
Registration System ◽  
Novel Approach ◽  
Centroid Distance ◽  
Curvature Scale Space ◽  
The World ◽  
Trademark Registration ◽  
Trademark Images

With the rapid increase in the amount of registered trademarks around the world, trademark image retrieval has been developed to deal with a vast amount of trademark images in a trademark registration system. Many different approaches have been developed throughout these years in an attempt to develop an effective TIR system. Some conventional approaches used in content-based image retrieval, such as moment invariants, Zernike moments, Fourier descriptors and curvature scale space descriptors, have also been widely used in TIR. These approaches, however, contain some major deficiencies when addressing the TIR problem. Therefore, this chapter proposes a novel approach in order to overcome the major deficiencies of the conventional approaches. The proposed approach combines the Zernike moments descriptors with the centroid distance representation and the curvature representation. The experimental results show that the proposed approach outperforms the conventional approaches in several circumstances. Details regarding to the proposed approach as well as the conventional approaches are presented in this chapter.

scholarly journals A Comparative Study of SIFT and its Variants

2013 ◽  
Vol 13 (3) ◽  
pp. 122-131 ◽  
Author(s):  
Jian Wu ◽  
Zhiming Cui ◽  
Victor S. Sheng ◽  
Pengpeng Zhao ◽  
Dongliang Su ◽  
...  
Keyword(s):  
Machine Vision ◽  
Comparative Study ◽  
Image Retrieval ◽  
Scale Space ◽  
Local Feature ◽  
Experimental Results ◽  
Image Stitching ◽  
Illumination Change ◽  
Feature Description ◽  
Scale Change

SIFT is an image local feature description algorithm based on scale-space. Due to its strong matching ability, SIFT has many applications in different fields, such as image retrieval, image stitching, and machine vision. After SIFT was proposed, researchers have never stopped tuning it. The improved algorithms that have drawn a lot of attention are PCA-SIFT, GSIFT, CSIFT, SURF and ASIFT. In this paper, we first systematically analyze SIFT and its variants. Then, we evaluate their performance in different situations: scale change, rotation change, blur change, illumination change, and affine change. The experimental results show that each has its own advantages. SIFT and CSIFT perform the best under scale and rotation change. CSIFT improves SIFT under blur change and affine change, but not illumination change. GSIFT performs the best under blur change and illumination change. ASIFT performs the best under affine change. PCA-SIFT is always the second in different situations. SURF performs the worst in different situations, but runs the fastest.

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