scholarly journals Image Retrieval Model Analysis of Digital Library Based on Texture Characteristics

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Yu Zhao
Keyword(s):  
Image Retrieval ◽  
Digital Library ◽  
Texture Features ◽  
Document Image ◽  
Retrieval Algorithm ◽  
Retrieval Model ◽  
Edge Information ◽  
Texture Characteristics ◽  
Retrieval Efficiency ◽  
Efficient Retrieval

A new document image retrieval algorithm is proposed in view of the inefficient retrieval of information resources in a digital library. First of all, in order to accurately characterize the texture and enhance the ability of image differentiation, this paper proposes the statistical feature method of the double-tree complex wavelet. Secondly, according to the statistical characteristic method, combined with the visual characteristics of the human eye, the edge information in the document image is extracted. On this basis, we construct the meaningful texture features and use texture features to define the characteristic descriptors of document images. Taking the descriptor as the clue, the content characteristics of the document image are combined organically, and appropriate similarity measurement criteria are used for efficient retrieval. Experimental results show that the algorithm not only has high retrieval efficiency but also reduces the complexity of the traditional document image retrieval algorithm.

An Image Retrieval Algorithm Base on Texture Features

2014 ◽  
Vol 668-669 ◽  
pp. 1041-1044
Author(s):  
Lin Lin Song ◽  
Qing Hu Wang ◽  
Zhi Li Pei
Keyword(s):  
Image Retrieval ◽  
Statistical Methods ◽  
Texture Features ◽  
Retrieval Algorithm ◽  
Occurrence Matrix ◽  
Algorithm Base

This paper firstly studies the texture features. We construct a gray-difference primitive co-occurrence matrix to extract texture features by combining statistical methods with structural ones. The experiment results show that the features of the gray-difference primitive co-occurrence matrix are more delicate than the traditional gray co-occurrence matrix.

scholarly journals Document image retrieval based on texture features and similarity fusion

2016 ◽  
Author(s):  
Fahimeh Alaei ◽  
Alireza Alaei ◽  
Michael Blumenstein ◽  
Umapada Pal
Keyword(s):  
Image Retrieval ◽  
Texture Features ◽  
Document Image

scholarly journals A comparative study of different texture features for document image retrieval

2019 ◽  
Vol 121 ◽  
pp. 97-114 ◽  
Author(s):  
Fahimeh Alaei ◽  
Alireza Alaei ◽  
Umapada Pal ◽  
Michael Blumenstein
Keyword(s):  
Comparative Study ◽  
Image Retrieval ◽  
Texture Features ◽  
Document Image

scholarly journals Improved CNN-Based Hashing for Encrypted Image Retrieval

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Wenyan Pan ◽  
Meimin Wang ◽  
Jiaohua Qin ◽  
Zhili Zhou
Keyword(s):  
Image Retrieval ◽  
Computational Cost ◽  
Image Data ◽  
Image Size ◽  
Encrypted Image ◽  
Compact Binary ◽  
Hash Code ◽  
Retrieval Efficiency ◽  
Efficient Retrieval ◽  
Encrypted Images

As more and more image data are stored in the encrypted form in the cloud computing environment, it has become an urgent problem that how to efficiently retrieve images on the encryption domain. Recently, Convolutional Neural Network (CNN) features have achieved promising performance in the field of image retrieval, but the high dimension of CNN features will cause low retrieval efficiency. Also, it is not suitable to directly apply them for image retrieval on the encryption domain. To solve the above issues, this paper proposes an improved CNN-based hashing method for encrypted image retrieval. First, the image size is increased and inputted into the CNN to improve the representation ability. Then, a lightweight module is introduced to replace a part of modules in the CNN to reduce the parameters and computational cost. Finally, a hash layer is added to generate a compact binary hash code. In the retrieval process, the hash code is used for encrypted image retrieval, which greatly improves the retrieval efficiency. The experimental results show that the scheme allows an effective and efficient retrieval of encrypted images.

Color Image Retrieval Algorithm Based on Dual Tree Complex Wavelet Transform

2013 ◽  
Vol 427-429 ◽  
pp. 1761-1764
Author(s):  
Xiao Li Zhao ◽  
Shu Jun Yin
Keyword(s):  
Wavelet Transform ◽  
Image Retrieval ◽  
Color Image ◽  
Texture Features ◽  
Retrieval Algorithm ◽  
Complex Wavelet Transform ◽  
Invariant Moments ◽  
Frequency Components ◽  
Complex Wavelet ◽  
Form Feature

A color image retrieval algorithm was presented in order to retrieve image from massive images. This method firstly transformed image from RGB space to HSV space. V component of HSV space was decomposed into three levels by dual tree complex wavelet transform (DT-CWT) to extract high frequency components as texture features. Invariant moments of V component were extracted as shape features. Information of H component of HSV space was extracted as color features. Then three types of features were combined to form feature vector to judge similarity of images. Experiments show that this algorithm has not only high precision and recall but also low time consumption.

An Image Retrieval Model Based on Local Fisher Discriminant Analysis

2011 ◽  
Vol 255-260 ◽  
pp. 2057-2061
Author(s):  
Yong Mao Wang
Keyword(s):  
Discriminant Analysis ◽  
Dimensionality Reduction ◽  
Image Retrieval ◽  
Texture Features ◽  
Retrieval Model ◽  
Fisher Discriminant Analysis ◽  
Model Based ◽  
Fisher Discriminant ◽  
Local Fisher Discriminant Analysis ◽  
Linear Dimensionality Reduction

This paper introduces an image retrieval model based on dimensionality reduction. The proposed model is divided into two main techniques, the first one is concerned with the feature extraction from image database, and the second one is performing a dimensionality reduction. In the first technique, the color histogram and Color Texture Moment are used to extract the color and texture features, respectively. In the second technique, Local Fisher Discriminant Analysis (LFDA) which is a supervised linear dimensionality reduction algorithm is used to performing dimensionality. LFDA combines the ideas of Fisher Discriminant Analysis (FDA) and Locality Preserving Projection (LPP). LFDA can preserve both manifold of data and discriminant information. Experiments demonstrate that the proposed image retrieval scheme based on dimensionality reduction can achieve satisfactory results.

Towards Mobile Document Image Retrieval for Digital Library

2014 ◽  
Vol 16 (2) ◽  
pp. 346-359 ◽  
Author(s):  
Ling-Yu Duan ◽  
Rongrong Ji ◽  
Zhang Chen ◽  
Tiejun Huang ◽  
Wen Gao
Keyword(s):  
Image Retrieval ◽  
Digital Library ◽  
Document Image

scholarly journals USING TEXTURE AND SHAPE FEATURES TO RETRIEVE SETS OF SIMILAR MEDICAL IMAGES

2003 ◽  
Vol 15 (05) ◽  
pp. 193-199 ◽  
Author(s):  
JIANN-DER LEE ◽  
LI-PENG LOU
Keyword(s):  
Standard Deviation ◽  
Image Retrieval ◽  
Wavelet Decomposition ◽  
Texture Features ◽  
Binary Codes ◽  
Shape Features ◽  
Retrieval Task ◽  
Texture Information ◽  
Retrieval Efficiency ◽  
The Mean

In this paper, a novel scheme has been proposed for image retrieval task using the feature extracted directly from a compressed or uncompressed image. The texture information is first extracted by exploiting the multiresolution nature of wavelet decomposition, which represent the horizontal, vertical and diagonal frequency distribution of an image. We then calculate the mean and standard deviation of wavelet coefficients of each sub-band as texture features. In additions, we also extract shape feature by using the fixed-resolution block representation, which divides the image into isometric blocks and calculate the overlapped degree of each block with binary codes. The experimental results show that the retrieval efficiency is considerably improved by the proposed approach.

scholarly journals Image Retrieval based Convolutional Neural Network

2020 ◽  
Vol 31 (4) ◽  
pp. 43
Author(s):  
Nuha Mohammed Khassaf ◽  
Shaimaa Hameed Shaker
Keyword(s):  
Neural Network ◽  
Image Retrieval ◽  
Convolutional Neural Network ◽  
Experimental Evaluation ◽  
Texture Features ◽  
Content Based Image Retrieval ◽  
Large Set ◽  
Agricultural Field ◽  
Query Image ◽  
Efficient Retrieval

At the present time, everyone is interested in dealing with images in different fields such as geographic maps, medical images, images obtaining by Camera, microscope, telescope, agricultural field photos, paintings, industrial parts drawings, space photos, etc. Content Based Image Retrieval (CBIR) is an efficient retrieval of relevant images from databases based on features extracted from the image. Follow the proposed system for retrieving images related to a query image from a large set of images, based approach to extract the texture features present in the image using statistical methods (PCA, MAD, GLCM, and Fusion) after pre-processing of images. The proposed system was trained using 1D CNN using a dataset Corel10k which widely used for experimental evaluation of CBIR performance the results of proposed system shows that the highest accuracy is 97.5% using Fusion (PCA, MAD), where the accuracy is 95% using MAD, 90% using PCA. The performance result is acceptable compared to previous work.

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