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 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 Binary Wavelet Transform Based Histogram Feature for Content Based Image Retrieval

2011 ◽  
pp. 68-75 ◽  
Author(s):  
Megha Agarwal ◽  
R. P. Maheshwari
Keyword(s):  
Wavelet Transform ◽  
Image Retrieval ◽  
Discrete Wavelet Transform ◽  
Content Based Image Retrieval ◽  
Natural Image ◽  
Discrete Wavelet ◽  
Average Precision ◽  
Average Recall ◽  
Retrieval Rate ◽  
Histogram Feature

In this paper a new visual feature, binary wavelet transform based histogram (BWTH) is proposed for content based image retrieval. BWTH is facilitated with the color as well as texture properties. BWTH exhibits the advantages of binary wavelet transform and histogram. The performance of CBIR system with proposed feature is observed on Corel 1000 (DB1) and Corel 2450 (DB2) natural image database in color as well as gray space. The results analysis of DB1 database illustrates the better average precision and average recall of proposed method in RGB space (73.82%, 44.29%) compared to color histogram (70.85%, 42.16%), auto correlogram (66.15%, 39.52%) and discrete wavelet transform (60.83%, 38.25%). In case of gray space also performance of proposed method (66.69%, 40.77%) is better compared to auto correlogram (57.20%, 35.31%), discrete wavelet transform (52.70%, 32.98%) and wavelet correlogram (64.3%, 38.0%). It is verified that in case of DB2 database also average precision, average recall and average retrieval rate of proposed method are significantly better.

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 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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