An effective hybrid framework for content based image retrieval (CBIR)

Khan, Umer Ali; Javed, Ali; Ashraf, Rehan

doi:10.1007/s11042-021-10530-x

Cited by 45 publications

(16 citation statements)

References 55 publications

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“…As shown in Table 1 , the overall calculation time required for the whole retrieval process for a CBIR in the proposed work is divided into feature extraction time, prediction time, and similarity measurement and sorting time. The comparison between the proposed framework and the recent CBIR approaches (GMM-EM [ 45 ], Hybrid features [ 46 ], CH-LDP [ 47 ], CH-LDP-SIFT-DPBoF [ 48 ], and GA-SVM [ 49 ]) in Table 5 proves the adequate performance of the suggested method. Furthermore, according to the same table, the proposed framework outperforms all other methods in the mean average precision.…”

Section: Resultsmentioning

confidence: 87%

Hybrid Bag-of-Visual-Words and FeatureWiz Selection for Content-Based Visual Information Retrieval

Bakheet

Al-Hamadi

Soliman

et al. 2023

Sensors

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Recently, content-based image retrieval (CBIR) based on bag-of-visual-words (BoVW) model has been one of the most promising and increasingly active research areas. In this paper, we propose a new CBIR framework based on the visual words fusion of multiple feature descriptors to achieve an improved retrieval performance, where interest points are separately extracted from an image using features from accelerated segment test (FAST) and speeded-up robust features (SURF). The extracted keypoints are then fused together in a single keypoint feature vector and the improved RootSIFT algorithm is applied to describe the region surrounding each keypoint. Afterward, the FeatureWiz algorithm is employed to reduce features and select the best features for the BoVW learning model. To create the codebook, K-means clustering is applied to quantize visual features into a smaller set of visual words. Finally, the feature vectors extracted from the BoVW model are fed into a support vector machines (SVMs) classifier for image retrieval. An inverted index technique based on cosine distance metric is applied to sort the retrieved images to the similarity of the query image. Experiments on three benchmark datasets (Corel-1000, Caltech-10 and Oxford Flower-17) show that the presented CBIR technique can deliver comparable results to other state-of-the-art techniques, by achieving average accuracies of 92.94%, 98.40% and 84.94% on these datasets, respectively.

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Section: Resultsmentioning

confidence: 87%

Hybrid Bag-of-Visual-Words and FeatureWiz Selection for Content-Based Visual Information Retrieval

Bakheet

Al-Hamadi

Soliman

et al. 2023

Sensors

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“…Umer et al 24 developed an efficient content-based image retrieval CBIR system capable of retrieving correct images semantically. They proposed a hybrid features descriptor consisting of color and texture features for this purpose.…”

Section: Related Workmentioning

confidence: 99%

Retrieving Encrypted Images Using Convolution Neural Network and Fully Homomorphic Encryption

Alsaedi,

Farhan

2023

Baghdad Sci.J

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A content-based image retrieval (CBIR) is a technique used to retrieve images from an image database. However, the CBIR process suffers from less accuracy to retrieve images from an extensive image database and ensure the privacy of images. This paper aims to address the issues of accuracy utilizing deep learning techniques as the CNN method. Also, it provides the necessary privacy for images using fully homomorphic encryption methods by Cheon, Kim, Kim, and Song (CKKS). To achieve these aims, a system has been proposed, namely RCNN_CKKS, that includes two parts. The first part (offline processing) extracts automated high-level features based on a flatting layer in a convolutional neural network (CNN) and then stores these features in a new dataset. In the second part (online processing), the client sends the encrypted image to the server, which depends on the CNN model trained to extract features of the sent image. Next, the extracted features are compared with the stored features using a Hamming distance method to retrieve all similar images. Finally, the server encrypts all retrieved images and sends them to the client. Deep-learning results on plain images were 97.94% for classification and 98.94% for retriever images. At the same time, the NIST test was used to check the security of CKKS when applied to Canadian Institute for Advanced Research (CIFAR-10) dataset. Through these results, researchers conclude that deep learning is an effective method for image retrieval and that a CKKS method is appropriate for image privacy protection.

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“…Khan et al (Khan et al, 2021) proposed an image retrieval mechanism based on support vector machine classifier and Genetic Algorithm (GA) as a feature descriptor. For feature extraction, they used Bi-Orthogonal wavelets, Daubechies Wavelet and Haar Wavelet for initial three color components.…”

Section: Related Workmentioning

confidence: 99%

Content-Based Image Retrieval Using Hybrid Densenet121-Bilstm and Harris Hawks Optimization Algorithm

Sanjeevaiah¹,

Reddy²,

Karthik³

et al. 2022

International Journal of Software Innovation

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In the field of digital data management, content-based image retrieval (CBIR) has become one of the most important research areas, and it is used in many fields. This system searches a database of images to retrieve most visually comparable photos to a query image. It is based on features derived directly from the image data, rather than on keywords or annotations. Currently, deep learning approaches have demonstrated a strong interest in picture recognition, particularly in extracting information about the features of the image. Therefore, a Densenet-121 is employed in this work to extract high-level and deep characteristics from the images. Afterwards, the training images are retrieved from the dataset and compared to the query image using a Bidirectional LSTM (BiLSTM) classifier to obtain the relevant images. The investigations are conducted using a publicly available dataset named Corel, and the f-measure, recall, and precision metrics are used for performance assessment. Investigation outcomes show that the proposed technique outperforms the existing image retrieval techniques.

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An effective hybrid framework for content based image retrieval (CBIR)

Cited by 45 publications

References 55 publications

Hybrid Bag-of-Visual-Words and FeatureWiz Selection for Content-Based Visual Information Retrieval

Hybrid Bag-of-Visual-Words and FeatureWiz Selection for Content-Based Visual Information Retrieval

Retrieving Encrypted Images Using Convolution Neural Network and Fully Homomorphic Encryption

Content-Based Image Retrieval Using Hybrid Densenet121-Bilstm and Harris Hawks Optimization Algorithm

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