scholarly journals Content-Based Image Retrieval System Based on Fusion of Wavelet Transform, Texture and Shape Features

2021 ◽  
Vol 8 (1) ◽  
pp. 110-116
Author(s):  
Dannina Kishore ◽  
Chanamallu Srinivasa Rao
Keyword(s):  
Wavelet Transform ◽  
Image Retrieval ◽  
Feature Fusion ◽  
Content Based Image Retrieval ◽  
Discrete Wavelet ◽  
Single Feature ◽  
Image Retrieval System ◽  
Efficient Retrieval ◽  
Multiple Resolutions ◽  
Similar Images

In the last few years, Content-Based Image Retrieval (CBIR) has received wide attention. Compared to text-based image retrieval contents of the image are more in information for efficient retrieval by Content-Based Image Retrieval. The single feature cannot be applied to all the images and provides lower performance. In this paper, we have put forward a proposal on an image retrieval using multi-feature fusion. The concept of multi-resolution has been exploited with the help of a wavelet transform. This method combines Local Binary Pattern (LBP) with Fast and Accurate Exponent Fourier Moments (FAEFM’s) with the wavelet decomposition of an image using multiple resolutions. In order to extract the feature of texture from image, LBP codes of Discrete Wavelet Transform (DWT), the image coefficients are estimated followed by the computation of Fast and Accurate Exponent Fourier Moments to these LBP codes so as to extract features of shape to construct the required feature vector. These constructed vectors aid us in exactly finding out and retrieving visually similar images from existing databases. The benchmark databases Corel-1k and Olivia 2688 are used to test the proposed method. The proposed method achieves 99.99% of precision and 93.15% of recall on Corel-1k database and 99.99% of precision and recall of 93.63% on Olivia-2688 database, which are higher than the existing methods.

Content Based Image Retrieval by Using Color Descriptor and Discrete Wavelet Transform

2018 ◽  
Vol 42 (3) ◽  
Author(s):  
Rehan Ashraf ◽  
Mudassar Ahmed ◽  
Sohail Jabbar ◽  
Shehzad Khalid ◽  
Awais Ahmad ◽  
...  
Keyword(s):  
Wavelet Transform ◽  
Image Retrieval ◽  
Discrete Wavelet Transform ◽  
Content Based Image Retrieval ◽  
Discrete Wavelet ◽  
Color Descriptor

scholarly journals Combined global and local semantic feature–based image retrieval analysis with interactive feedback

2019 ◽  
Vol 53 (1-2) ◽  
pp. 3-17
Author(s):  
A Anandh ◽  
K Mala ◽  
R Suresh Babu
Keyword(s):  
Wavelet Transform ◽  
Image Retrieval ◽  
Research Work ◽  
Research Area ◽  
Content Based Image Retrieval ◽  
Image Retrieval System ◽  
False Image ◽  
Active Research ◽  
High Level ◽  
Interactive Feedback

Nowadays, user expects image retrieval systems using a large database as an active research area for the investigators. Generally, content-based image retrieval system retrieves the images based on the low-level features, high-level features, or the combination of both. Content-based image retrieval results can be improved by considering various features like directionality, contrast, coarseness, busyness, local binary pattern, and local tetra pattern with modified binary wavelet transform. In this research work, appropriate features are identified, applied and results are validated against existing systems. Modified binary wavelet transform is a modified form of binary wavelet transform and this methodology produced more similar retrieval images. The proposed system also combines the interactive feedback to retrieve the user expected results by addressing the issues of semantic gap. The quantitative evaluations such as average retrieval rate, false image acceptation ratio, and false image rejection ratio are evaluated to ensure the user expected results of the system. In addition to that, precision and recall are evaluated from the proposed system against the existing system results. When compared with the existing content-based image retrieval methods, the proposed approach provides better retrieval accuracy.

scholarly journals An Efficient Two-layer based Technique for Content-based Image Retrieval

2021 ◽  
Vol 5 (1) ◽  
pp. 28
Author(s):  
Fawzi Abdul Azeez Salih ◽  
Alan Anwer Abdulla
Keyword(s):  
Image Retrieval ◽  
Texture Features ◽  
Superior Performance ◽  
Content Based Image Retrieval ◽  
Discrete Wavelet ◽  
Color Spaces ◽  
Query Image ◽  
Current State ◽  
Similar Images ◽  
Different Color

The rapid advancement and exponential evolution in the multimedia applications raised the attentional research on content-based image retrieval (CBIR). The technique has a significant role for searching and finding similar images to the query image through extracting the visual features. In this paper, an approach of two layers of search has been developed which is known as two-layer based CBIR. The first layer is concerned with comparing the query image to all images in the dataset depending on extracting the local feature using bag of features (BoF) mechanism which leads to retrieve certain most similar images to the query image. In other words, first step aims to eliminate the most dissimilar images to the query image to reduce the range of search in the dataset of images. In the second layer, the query image is compared to the images obtained in the first layer based on extracting the (texture and color)-based features. The Discrete Wavelet Transform (DWT) and Local Binary Pattern (LBP) were used as texture features. However, for the color features, three different color spaces were used, namely RGB, HSV, and YCbCr. The color spaces are utilized by calculating the mean and entropy for each channel separately. Corel-1K was used for evaluating the proposed approach. The experimental results prove the superior performance of the proposed concept of two-layer over the current state-of-the-art techniques in terms of precision rate in which achieved 82.15% and 77.27% for the top-10 and top-20, respectively.

scholarly journals A Novel Adaptive Feature Fusion Strategy for Image Retrieval

Entropy ◽  
2021 ◽  
Vol 23 (12) ◽  
pp. 1670
Author(s):  
Xiaojun Lu ◽  
Libo Zhang ◽  
Lei Niu ◽  
Qing Chen ◽  
Jianping Wang
Keyword(s):  
Image Retrieval ◽  
Information Entropy ◽  
Retrieval System ◽  
Feature Fusion ◽  
Research Direction ◽  
Image Features ◽  
Retrieval Strategy ◽  
Fusion Image ◽  
Single Feature ◽  
Image Retrieval System

In the era of big data, it is challenging to efficiently retrieve the required images from the vast amount of data. Therefore, a content-based image retrieval system is an important research direction to address this problem. Furthermore, a multi-feature-based image retrieval system can compensate for the shortage of a single feature to a certain extent, which is essential for improving retrieval system performance. Feature selection and feature fusion strategies are critical in the study of multi-feature fusion image retrieval. This paper proposes a multi-feature fusion image retrieval strategy with adaptive features based on information entropy theory. Firstly, we extract the image features, construct the distance function to calculate the similarity using the information entropy proposed in this paper, and obtain the initial retrieval results. Then, we obtain the precision of single feature retrieval based on the correlation feedback as the retrieval trust and use the retrieval trust to select the effective features automatically. After that, we initialize the weights of selected features using the average weights, construct the probability transfer matrix, and use the PageRank algorithm to update the initialized feature weights to obtain the final weights. Finally, we calculate the comprehensive similarity based on the final weights and output the detection results. This has two advantages: (1) the proposed strategy uses multiple features for image retrieval, which has better performance and more substantial generalization than the retrieval strategy based on a single feature; (2) compared with the fixed-feature retrieval strategy, our method selects the best features for fusion in each query, which takes full advantages of each feature. The experimental results show that our proposed method outperforms other methods. In the datasets of Corel1k, UC Merced Land-Use, and RSSCN7, the top10 retrieval precision is 99.55%, 88.02%, and 88.28%, respectively. In the Holidays dataset, the mean average precision (mAP) was 92.46%.

scholarly journals Content Based Image Retrieval using Transform Domain Features and Algorithms

2019 ◽  
Vol 9 (1) ◽  
pp. 3008-3011
Keyword(s):  
Wavelet Transform ◽  
Image Retrieval ◽  
Distance Measures ◽  
Content Based Image Retrieval ◽  
Computational Time ◽  
Discrete Wavelet ◽  
Transform Domain ◽  
Integer Wavelet ◽  
Mean Variance ◽  
Efficient Recognition

In this paper, Content Based Image Retrieval using Transform domain features and algorithms has been implemented. The image can be decomposed by Discrete Wavelet Transform (DWT) to extract the features based on DC coefficients. Each sub-image is calculated by mean, variance and standard deviation to get more efficient recognition. The database image also applied in the domain of Stationary Wavelet Transform (SWT) and Integer Wavelet Transform (IWT) by using different distance measures. The proposed algorithm is the combination of DWT, SWT and IWT has been implemented using COREL database. This proposed method has more efficient recognition and less computational time over existing methods

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