PZMLIR: Pseudo-Zernike Moments and LSH-based Image Retrieval in P2P datacenter

2013 ◽  
Author(s):  
Di Yang ◽  
Lejian Zhang ◽  
Jing Wang ◽  
Jianxin Liao
Keyword(s):  
Image Retrieval ◽  
Zernike Moments

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.

An efficient and robust approach for biomedical image retrieval using Zernike moments

2018 ◽  
Vol 39 ◽  
pp. 459-473 ◽  
Author(s):  
Yogesh Kumar ◽  
Ashutosh Aggarwal ◽  
Shailendra Tiwari ◽  
Karamjeet Singh
Keyword(s):  
Image Retrieval ◽  
Zernike Moments ◽  
Robust Approach ◽  
Biomedical Image ◽  
Biomedical Image Retrieval

scholarly journals A Comparative Analysis of the Zernike Moments for Single Object Retrieval

2019 ◽  
Vol 16 (2(SI)) ◽  
pp. 0504 ◽  
Author(s):  
Abu Bakar Et al.
Keyword(s):  
Image Retrieval ◽  
Shape Representation ◽  
Outer Boundary ◽  
Zernike Moments ◽  
Zernike Moment ◽  
Experimental Investigations ◽  
Object Image ◽  
Single Object ◽  
Object Retrieval ◽  
Object Shape

Zernike Moments has been popularly used in many shape-based image retrieval studies due to its powerful shape representation. However its strength and weaknesses have not been clearly highlighted in the previous studies. Thus, its powerful shape representation could not be fully utilized. In this paper, a method to fully capture the shape representation properties of Zernike Moments is implemented and tested on a single object for binary and grey level images. The proposed method works by determining the boundary of the shape object and then resizing the object shape to the boundary of the image. Three case studies were made. Case 1 is the Zernike Moments implementation on the original shape object image. In Case 2, the centroid of the shape object image in Case 1 is relocated to the center of the image. In Case 3, the proposed method first detect the outer boundary of the shape object and then resizing the object to the boundary of the image. Experimental investigations were made by using two benchmark shape image datasets showed that the proposed method in Case 3 had demonstrated to provide the most superior image retrieval performances as compared to both the Case 1 and Case 2. As a conlusion, to fully capture the powerful shape representation properties of the Zernike moment, a shape object should be resized to the boundary of the image.

An Effective Solution for Trademark Retrieval by Combining Pseudo Zernike Moments and SIFT Features

2012 ◽  
Vol 468-471 ◽  
pp. 777-780
Author(s):  
Zhen Hai Wang
Keyword(s):  
Image Retrieval ◽  
High Precision ◽  
Retrieval System ◽  
Zernike Moments ◽  
Retrieval Algorithm ◽  
Effective Solution ◽  
Global Features ◽  
Query Image ◽  
Image Retrieval System ◽  
Sift Features

This paper proposes a trademark retrieval algorithm combining the image global features and local features. Firstly, extract pseudo Zernike moments of the retrieved image and sort them according to similarity. Candidate images are formed. Then, the SIFT features are used for matching the query image accurately with candidate images. Experimental results show that this method not only keeps high precision- recall of SIFT features and is superior than the method based on the single pseudo Zernike moments feature, but also improves effective retrieval speed compared to the single SIFT features. This method can be well applied to the trademark image retrieval system.

Shape Based Image Retrieval using Lower Order Zernike Moments

2017 ◽  
Vol 7 (3) ◽  
pp. 1651 ◽  
Author(s):  
G. Sucharitha ◽  
Ranjan K. Senapati
Keyword(s):  
Image Retrieval ◽  
Lower Order ◽  
Local Information ◽  
Zernike Moments ◽  
Zernike Moment ◽  
Shape Classification ◽  
Shape Features ◽  
Similar Images ◽  
Novel Method ◽  
Concentric Circles

Shape is one of the significant features of Content Based Image Retrieval (CBIR). This paper proposes a strong and successful shape feature, which is based on a set of orthogonal complex moments of images known as Zernike moments. For shape classification Zernike moment (ZM) is the dominant solution. The radial polynomial of Zernike moment produces the number of concentric circles based on the order. As the order increases number of circles will increases, due to this the local information of an image will be ignored. In this paper, we introduced a novel method for radial polynomial where local information of an image given importance. We succeeded to extract the local features and shape features at very a low order of polynomial compared to the state of traditional ZM.The proposed method gives an advantage of a lower order, less complex, and lower dimension feature vector.For more similar images we find that simple  Euclidian distance approximately zero. Proposed method tested on a MPEG-7 CE-1 shape database, Coil-100 databases. Experiments demonstrated that it is outperforming in identifying the shape of an object in the image  and reduced the retrieving time and complexity of calculations.

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