Classified-Distance Based Shape Descriptor for Application to Image Retrieval

2013 ◽  
pp. 1-8 ◽  
Author(s):  
Jinhee Chun ◽  
Natsuda Kaothanthong ◽  
Takeshi Tokuyama
Keyword(s):  
Image Retrieval ◽  
Shape Descriptor

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.

Downsizing the Semantic Gap in Contextual Image Retrieval System Using Superintend Gross Silhouette Descriptor

2020 ◽  
Vol 8 (4) ◽  
pp. 1-20
Author(s):  
Girija G. Chiddarwar ◽  
S.Phani Kumar
Keyword(s):  
Image Retrieval ◽  
Correlation Coefficient ◽  
Retrieval System ◽  
Shape Descriptor ◽  
Query Image ◽  
Relationship Strength ◽  
Image Retrieval System ◽  
High Level ◽  
Straight Lines ◽  
The Relationship

Since shape is the most important feature for recognizing objects, it has to be extracted accurately in order to enhance the content based image retrieval system, but challenges prevailed in extracting shape features of an object in an image due to inability of shape descriptor which extracts a limited number of different shapes that are not invariant, alongside the inability to extracting features of overlapping objects, and the shape connotation gap problem between low level and high level features. In order to overcome these problems, this work proposes a Superintend Gross Silhouette Descriptor which uses pixel coordinates on spatial domain of the image for finding the real shape of the object by means of straight lines so it has the ability to detect the overlapped objects as well as the polygonal shapes. After being extracted, features would be trained using a random woodland classifier which classifies the features into a group of classes at maximum convergence for mitigating the shape connotation problem. At the time of retrieval, the features of the query image would be tested with trained features for measuring the similarity by the dynamite correlation coefficient method, which is a measure of the linear correlation so it would render the absolute value of the correlation coefficient which maintains the relationship strength among features.

Alumina Sintering Kiln Flame Image Retrieval Based on ANN and Flame Shape Feature

2009 ◽  
Vol 4 (1) ◽  
Author(s):  
Hongliang Zhang ◽  
Jie Li ◽  
Zhong Zou
Keyword(s):  
Image Retrieval ◽  
Rotary Kiln ◽  
Shape Descriptor ◽  
High Accuracy ◽  
Retrieval Algorithm ◽  
Shape Descriptors ◽  
Shape Features ◽  
Retrieval Method ◽  
Flame Shape ◽  
Artificial Neural Network Ann

An alumina sintering rotary kiln flame image retrieval method was put forward based on artificial neural network (ANN) and flame shape features. An effective flame shape descriptor was introduced, based on which the flame image recognitions were carried out using ANN. Then, a flame image retrieval algorithm was designed. Experiments were carried out on the prototype machine with the flame images sampled from an alumina sintering rotary kiln. The results indicate that the shape descriptors can effectively describe the flame shapes and the proposed flame image retrieval method can achieve both high accuracy and efficiency. This method can be of promising theoretical and practical value for alumina sintering rotary kiln management and surveillance.

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