Research and Implement the Image Retrieval Method Based on the Shape Feature

2011 ◽  
Vol 301-303 ◽  
pp. 1048-1051
Author(s):  
Xiao Juan Guo ◽  
Quan Rui Wang ◽  
Chang Jiang Li ◽  
Yun Juan Liang
Keyword(s):  
Image Retrieval ◽  
Image Database ◽  
Features Extraction ◽  
Shape Feature ◽  
Shape Features ◽  
Distance Metric ◽  
Retrieval Method ◽  
Similarity Metric

The paper has research and analyzed the arithmetic of shape features extraction and similarity metric, and adopted the Euclid distance metric, and according to the image database of the bird which from UIUC and the database of chair, strawberry from Caltech 101, the Hu invariants moments features extraction are validated. Compared these experiment results, at the different database, some image which has simple background and different shape object can be obtained the better retrieval effect.

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.

scholarly journals Integration of Statistical based Texture and Color Feature for Medical Image Retrieval

2019 ◽  
Vol 8 (3) ◽  
pp. 5584-5588 ◽  
Keyword(s):  
Computer Vision ◽  
Pattern Recognition ◽  
Image Retrieval ◽  
Medical Image ◽  
Spatial Information ◽  
Image Database ◽  
Content Based Image Retrieval ◽  
Retrieval Method ◽  
Medical Image Retrieval ◽  
The Common

Today, the common problem in the domain of computer vision and pattern recognition is content based image retrieval (CBIR). In this paper, a novel image retrieval method using the geometric details based on the correlation among edgels and correlation between pixels has been introduced. The autocorrelation based choridiogram descriptor has been extracted from the image to obtain geometric, texture and spatial information. Color autocorrelogram has been computed to obtain color, texture and spatial information. The proposed method is tested on benchmark heterogeneous medical image database and LIDC-IDRI-CT and VIA/I-ELCAP-CT databases and results are compared with typical CBIR system for medical image retrieval

Visual Pattern Based Compressed Domain Image Retrieval

2009 ◽  
pp. 441-447
Author(s):  
Gerald Schaefer
Keyword(s):  
Image Compression ◽  
Image Retrieval ◽  
Image Database ◽  
Compression Algorithm ◽  
Visual Pattern ◽  
Compressed Domain ◽  
Retrieval Method ◽  
The Past ◽  
Effectiveness And Efficiency

While image retrieval and image compression have been pursued separately in the past, compressed domain techniques, which allow processing or retrieval of images without prior decompression, are becoming increasingly important. In this chapter we show that such midstream content access is possible and present a compressed domain retrieval method based on a visual pattern based compression algorithm. Experiments conducted on a medium sized image database demonstrate the effectiveness and efficiency of the presented approach.

Medical Image Retrieval Based on Simplified Multi-Wavelet Transform and Shape Feature

2014 ◽  
Vol 513-517 ◽  
pp. 2871-2875
Author(s):  
Xin Rui Wang ◽  
Yun Feng Yang
Keyword(s):  
Wavelet Transform ◽  
Image Retrieval ◽  
High Frequency ◽  
Medical Image ◽  
Shape Feature ◽  
Retrieval Strategy ◽  
Canny Operator ◽  
Retrieval Method ◽  
Medical Image Retrieval ◽  
Contour Lines

A novel medical image retrieval method based on Simplified Multi-wavelet Transform and Shape Feature was proposed in the paper, which included coarse and fine retrieval procedure. In the procedure of the coarse retrieval, Canny operator was used to extract edges of images. Moreover, contour lines were obtained by using the method of scan lines. At last, the coarse retrieval results of the images can be accomplished by using contour lines of images. In the procedure of the fine retrieval, the simplified multi-wavelet transform was used to decompose images at first, then, only the high frequency coefficients in the vertical directions were selected as retrieval objects. And hierarchical retrieval strategy was selected to accomplish the fine retrieval. This method not only can reduce the computational complexity effectively, but also can make full use of high frequency information of original images. Experiments showed that the accuracy of the retrieved results can be ensured.

Image Retrieval Based on Semi-Supervised Orthogonal Discriminant Embedding

2013 ◽  
Vol 347-350 ◽  
pp. 3532-3536
Author(s):  
Chuan Bo Huang ◽  
Li Xiang
Keyword(s):  
Dimensionality Reduction ◽  
Image Retrieval ◽  
Geometric Structure ◽  
Image Database ◽  
Label Propagation ◽  
Experimental Results ◽  
Retrieval Algorithm ◽  
Shape Features ◽  
Retrieval Performance ◽  
Discrimination Power

An retrieval algorithm based on dimensionality reduction is proposed to effectively extract the features to improve the performance of image retrieval. Firstly, the most important properties of the subspaces with respect to image retrieval is captured by intelligently utilizing the similarity and dissimilarity information of semantic and geometric structure in image database. Secondly, We propose Semi-supervised Orthogonal Discriminant Embedding Label Propagation method (SODELP) for image retrieval. The experimental results show that our method has the discrimination power against colour, texture and shape features and has good retrieval performance.

Tomato Disease Image Retrieval Based on Composite Features

2014 ◽  
Vol 571-572 ◽  
pp. 777-780
Author(s):  
Qian Zhang ◽  
Feng Yu ◽  
Xin Liu ◽  
Jun Feng Zhang
Keyword(s):  
Feature Extraction ◽  
Image Retrieval ◽  
New Method ◽  
Shape Features ◽  
Image Content ◽  
Retrieval Method ◽  
Hash Algorithm ◽  
Composite Feature ◽  
Tomato Disease ◽  
Color Texture

A new method of tomato disease image retrieval was proposed, which based on the composite feature extraction of disease images. The feature was converted to a set of hash sequence. This retrieval method can reflect the image content in a better way for the comprehensive application of color, texture and shape features. The digital index of image through perception hash algorithm processing can retrieval images and return the result more rapidly.

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