Content-Based Image Retrieval Using SOM and DWT

Huneiti, Ammar; Daoud, Maisa

doi:10.4236/jsea.2015.82007

Cited by 22 publications

(5 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The feature matching procedure was based on the Canberra distance. A CBIR method was proposed in [8] by extracting both color and texture feature vectors using the discrete Wavelet transform and the Self Organizing Map (SOM) artificial neural networks. At query time, texture vectors were compared using a similarity measure of the Euclidean distance, and the most similar image was retrieved.…”

Section: -Related Workmentioning

confidence: 99%

Content-based Retrieval of Tiles and Ceramics Images based on Grouping of Images and Minimal Feature Extraction

RajaeeNejad,

Mohanna

2023

jist

View full text Add to dashboard Cite

One of the most important databases in the e-commerce is tile and ceramic database, for which no specific retrieval method has been provided so far. In this paper, a method is proposed for the content-based retrieval of digital images of tiles and ceramics databases. First, a database is created by photographing different tiles and ceramics on the market from different angles and directions, including 520 images. Then a query image and the database images are divided into nine equal sub-images and all are grouped based on their sub-images. Next, the selected color and texture features are extracted from the sub-images of the database images and query image, so, each image has a feature vector. The selected features are the minimum features that are required to reduce the amount of computations and information stored, as well as speed up the retrieval. Average precision is calculated for the similarity measure. Finally, comparing the query feature vector with the feature vectors of all database images leads to retrieval. According to the retrieving results by the proposed method, its accuracy and speed are improved by 16.55% and 23.88%, respectively, compared to the most similar methods.

show abstract

Section: -Related Workmentioning

confidence: 99%

Content-based Retrieval of Tiles and Ceramics Images based on Grouping of Images and Minimal Feature Extraction

RajaeeNejad,

Mohanna

2023

jist

View full text Add to dashboard Cite

show abstract

“…Some other studies use feature selection or dimensionality reduction in order to increase the performance of the classification and retrieval tasks. For example, Huneiti and Daoud 23 proposed a CBIR method by using the discrete wavelet transform (DWT) and the self organizing map (SOM) artificial neural networks. The results showed that the proposed method is more accurate than other methods that use only the texture features.…”

Section: Related Workmentioning

confidence: 99%

A new method for image classification and image retrieval using convolutional neural networks

Giveki

Shakarami²,

Tarrah

et al. 2021

Concurrency and Computation

View full text Add to dashboard Cite

This article proposes a new method for image classification and image retrieval. The advantages of the proposed method are its high performance and requiring less memory compared to other methods. In order to extract image features, a Convolutional Neural Network (CNN), AlexNet, has been used. For image classification, we design a committee of four classifiers trained on graphics cards, narrowing the gap to human performance. For image retrieval, the similarity between extracted features from dataset images and features of the query image is calculated and the final results are visualized. Comprehensive experiments on Corel‐1k, Corel‐10k, Caltech‐101 object and Scene‐67 datasets have been investigated to find optimal parameters of the proposed method. The experiments demonstrate the high performance of the proposed method in comparison with the state‐of‐the‐art in the field.

show abstract

“…Paper have advantage of minimum searching time and maximum image retrieval results. In another work Huneiti et al [19] proposed the Discrete Wavelet Transform and Self Organizing Map (SOM) for efficient image retrieving. Retrieving mainly depends comparing of important features without comparing whole image features.…”

Section: Related Workmentioning

confidence: 99%

Content Based CT Image Sign Retrieval using Fast Discrete Curvelet Transform and Deep Learning

Biswas¹,

Mahavidyalaya²

2019

IJATCSE

View full text Add to dashboard Cite

In the world, one of the most critical diseases is lung cancer that leads to death of almost all affected human beings due to uncontrolled growth in the cell. These abnormal cells grow rapidly and divide to form tumor in the lungs. For lung cancer detection, the CAD system divided in four parts in CT images, such as noise removing process, segmentation of lung, detection of lung nodule and classification. The Visual information of similar nodules helps radiologists to detect the disease. This paper contains the Content-Based Image Retrieval (CBIR) system which is used for nodule retrieval in lung CT images. The CBIR system of pulmonary nodules retrieval system consists of pre-processing, feature extraction, feature selection, retrieval and classification. Our proposed method used manual cropping for segmentation phase to extract Region of Interest (ROI). The Gray-Level Co-Occurrence Matrix (GLCM) is used to extract features and the extraction of feature is done by Fast Discrete Curvelet Transform (FDCT). To select the perfect features, enhanced moth flame optimization algorithm is used and other best features are filtered by Deep Neural Networks (DNN's). We used the Euclidean distance to retrieve similar lung nodules from ROI database. The proposed method has been tested on the LISS database. Finally, we have achieved the content based image retrieval from ROI image database.

show abstract

Content-Based Image Retrieval Using SOM and DWT

Cited by 22 publications

References 15 publications

Content-based Retrieval of Tiles and Ceramics Images based on Grouping of Images and Minimal Feature Extraction

Content-based Retrieval of Tiles and Ceramics Images based on Grouping of Images and Minimal Feature Extraction

A new method for image classification and image retrieval using convolutional neural networks

Content Based CT Image Sign Retrieval using Fast Discrete Curvelet Transform and Deep Learning

Contact Info

Product

Resources

About