scholarly journals Integrating Pixel Cluster Indexing, Histogram Intersection And Discrete Wavelet Transform Methods For Color Images Content Based Image Retrieval System

2010 ◽  
pp. 345-352 ◽  
Author(s):  
Pijush Kanti Bhattacharjee
Keyword(s):  
Wavelet Transform ◽  
Image Retrieval ◽  
Discrete Wavelet Transform ◽  
Retrieval System ◽  
Color Images ◽  
Content Based Image Retrieval ◽  
Discrete Wavelet ◽  
Transform Methods ◽  
Histogram Intersection ◽  
Image Retrieval System

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

CONTENT BASED IMAGE RETRIEVAL USING DISCRETE WAVELET TRANSFORM

2004 ◽  
Vol 18 (01) ◽  
pp. 19-32 ◽  
Author(s):  
SAEID BELKASIM ◽  
XIANYU HONG ◽  
O. BASIR
Keyword(s):  
Wavelet Transform ◽  
Image Retrieval ◽  
Discrete Wavelet Transform ◽  
Retrieval System ◽  
Industrial Applications ◽  
Discrete Wavelet ◽  
Retrieval Process ◽  
System A ◽  
Image Transforms ◽  
The Common

Image retrieval plays an important role in a broad spectrum of applications. Contentbased retrieval (CBR) is one of the popular choices in many biomedical and industrial applications. Discrete image transforms have been widely studied and suggested for many image retrieval applications. The Discrete Wavelet Transform (DWT) is one of the most popular transforms recently applied to many image processing applications. The Daubechies wavelet can be used to form the basis for extracting features in retrieving images based on the description of a particular object within the scene. This wavelet is widely used for image compression. In this paper we highlight the common features between compression and retrieval. Several examples are used to test the DWT retrieval system. A comparison between DWT and Discrete Cosine Transform (DCT) is also made. The retrieval system using DWT requires preprocessing and normalization of images, which might slow down the retrieval process. The accuracy of the retrieval using DWT has been significantly improved by incorporating efficient K-Neighbor Nearest Distance (KNND) measure in our system.

scholarly journals Integration of Discrete Wavelet Transform, DBSCAN, and Classifiers for Efficient Content Based Image Retrieval

Electronics ◽  
2020 ◽  
Vol 9 (11) ◽  
pp. 1886
Author(s):  
Muhammad Junaid Khalid ◽  
Muhammad Irfan ◽  
Tariq Ali ◽  
Muqaddas Gull ◽  
Umar Draz ◽  
...  
Keyword(s):  
Wavelet Transform ◽  
Image Retrieval ◽  
Discrete Wavelet Transform ◽  
Feature Vector ◽  
Similarity Index ◽  
Machine Learning Algorithms ◽  
Image Feature ◽  
Content Based Image Retrieval ◽  
Support Vector ◽  
Discrete Wavelet

In the domain of computer vision, the efficient representation of an image feature vector for the retrieval of images remains a significant problem. Extensive research has been undertaken on Content-Based Image Retrieval (CBIR) using various descriptors, and machine learning algorithms with certain descriptors have significantly improved the performance of these systems. In this proposed research, a new scheme for CBIR was implemented to address the semantic gap issue and to form an efficient feature vector. This technique was based on the histogram formation of query and dataset images. The auto-correlogram of the images was computed w.r.t RGB format, followed by a moment’s extraction. To form efficient feature vectors, Discrete Wavelet Transform (DWT) in a multi-resolution framework was applied. A codebook was formed using a density-based clustering approach known as Density-Based Spatial Clustering of Applications with Noise (DBSCAN). The similarity index was computed using the Euclidean distance between the feature vector of the query image and the dataset images. Different classifiers, like Support Vector (SVM), K-Nearest Neighbor (KNN), and Decision Tree, were used for the classification of images. The set experiment was performed on three publicly available datasets, and the performance of the proposed framework was compared with another state of the proposed frameworks which have had a positive performance in terms of accuracy.

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.

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