Object-Based Image Retrieval Using Hierarchical Shape Descriptor

2002 ◽  
pp. 165-174 ◽  
Author(s):  
Man-Wai Leung ◽  
Kwok-Leung Chan
Keyword(s):  
Image Retrieval ◽  
Shape Descriptor ◽  
Object Based

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.

scholarly journals Object Based Image Retrieval from a Repository

2019 ◽  
Vol 9 (2S) ◽  
pp. 550-554
Keyword(s):  
Image Retrieval ◽  
World Wide ◽  
Storage Capacity ◽  
Imaging System ◽  
Digital World ◽  
Digital Storage ◽  
Object Properties ◽  
Object Based ◽  
Similar Images ◽  
Similar Kind

Today is a digital world. Due to the increase in imaging system, digital storage capacity and internetworking technology Content Based Retrieval of Images (CBIR) has become a vibrant research spot. The CBIR systems helps user to browse and retrieve similar kind of images from huge databases and World Wide Web. The Object based Image Retrieval (OBIR) Systems are the extension to the CBIR technique where it retrieves the similar images based on the object properties. So far massive amount of work has been done in this field of research. A plenty of the techniques and algorithms are published in the different papers. This paper provides brief survey on basic and recent approaches and techniques explained in different papers.

An Image Clustering and Feedback-Based Retrieval Framework

2012 ◽  
pp. 62-80
Author(s):  
Chengcui Zhang ◽  
Liping Zhou ◽  
Wen Wan ◽  
Jeffrey Birch ◽  
Wei-Bang Chen
Keyword(s):  
Image Retrieval ◽  
Relevance Feedback ◽  
Time Complexity ◽  
Clustering Algorithm ◽  
Image Database ◽  
Image Clustering ◽  
Image Region ◽  
Retrieval Systems ◽  
Object Based ◽  
Region Matching

Most existing object-based image retrieval systems are based on single object matching, with its main limitation being that one individual image region (object) can hardly represent the user’s retrieval target, especially when more than one object of interest is involved in the retrieval. Integrated Region Matching (IRM) has been used to improve the retrieval accuracy by evaluating the overall similarity between images and incorporating the properties of all the regions in the images. However, IRM does not take the user’s preferred regions into account and has undesirable time complexity. In this article, we present a Feedback-based Image Clustering and Retrieval Framework (FIRM) using a novel image clustering algorithm and integrating it with Integrated Region Matching (IRM) and Relevance Feedback (RF). The performance of the system is evaluated on a large image database, demonstrating the effectiveness of our framework in catching users’ retrieval interests in object-based image retrieval.

An Image Clustering and Feedback-based Retrieval Framework

2010 ◽  
Vol 1 (1) ◽  
pp. 55-74 ◽  
Author(s):  
Chengcui Zhang ◽  
Liping Zhou ◽  
Wen Wan ◽  
Jeffrey Birch ◽  
Wei-Bang Chen
Keyword(s):  
Image Retrieval ◽  
Clustering Algorithm ◽  
Image Database ◽  
Image Clustering ◽  
Single Object ◽  
Image Region ◽  
Retrieval Accuracy ◽  
Retrieval Systems ◽  
Object Based ◽  
Region Matching

Most existing object-based image retrieval systems are based on single object matching, with its main limitation being that one individual image region (object) can hardly represent the user’s retrieval target, especially when more than one object of interest is involved in the retrieval. Integrated Region Matching (IRM) has been used to improve the retrieval accuracy by evaluating the overall similarity between images and incorporating the properties of all the regions in the images. However, IRM does not take the user’s preferred regions into account and has undesirable time complexity. In this article, we present a Feedback-based Image Clustering and Retrieval Framework (FIRM) using a novel image clustering algorithm and integrating it with Integrated Region Matching (IRM) and Relevance Feedback (RF). The performance of the system is evaluated on a large image database, demonstrating the effectiveness of our framework in catching users’ retrieval interests in object-based image retrieval.

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