Feature fusion method using BoVW framework for enhancing image retrieval

Vimina, E. R.; Jacob, K. Poulose

doi:10.1049/iet-ipr.2018.5381

Cited by 16 publications

(6 citation statements)

References 22 publications

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“…The BoVW model is a promotion of the Bags of Words model from natural language processing and analysis to image processing and analysis [22]. The main idea of the BoVW model is to create a dictionary composed of visual words.…”

Section: Bovw Algorithmmentioning

confidence: 99%

Research on defect detection method of bearing dust cover based on machine vision and multi-feature fusion algorithm

Hao

Zhang

2023

Meas. Sci. Technol.

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During the assembly process of deep groove ball bearings, due to defective parts and unqualified assembly process, various indentations and scratches on the dust cover will often result in reducing the service life and reliability of the bearing. Therefore, the online monitoring of the assembly quality of the dust cover ensures the necessary detection process of the bearing surface quality. This paper proposed a bearing dust cover defect detection method based on machine vision and multi-feature fusion algorithm, which can effectively detect bearings with dust cover defects. The algorithm first performs Laplace transform and Sobel operator image enhancement on the collected bearing images. Extract and fuse multi-source fault feature with SIFT-BoVW and GLCM-Hu methods. Machine learning and deep learning models were constructed, and the performance of each model was compared through feature visualization and misclassified analysis. The results show that the extracted multi-source features are more representative and robust. The SIFT-BoVW-GS-SVM model achieved the best detection results in detecting bearing dust cover defects with an accuracy of 91.11%. The processing and program detection time for each bearing image is about 0.019s. The accuracy and speed of detection and judgment meet the needs of online defect detection of bearing dust cover.

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Section: Bovw Algorithmmentioning

confidence: 99%

Research on defect detection method of bearing dust cover based on machine vision and multi-feature fusion algorithm

Hao

Zhang

2023

Meas. Sci. Technol.

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“…ML or DL models can be fed directly with SURF features. However, more successful results were obtained in studies [44][45][46][47] that applied SURF features together with the BoVW model. For the detection of SURF key points, the Hessian matrix is used.…”

Section: Feature Extractionmentioning

confidence: 99%

Comparative Analysis of CNN Models and Bayesian Optimization-Based Machine Learning Algorithms in Leaf Type Classification

Aslan

2023

Balkan Journal of Electrical and Computer Engineering

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In this study, the leaves are classified by various Machine Learning (ML) and Deep Learning (DL) based Convolutional Neural Networks (CNN) methods. In the proposed method, first, image pre-processing is performed to increase the accuracy of the posterior process. The obtained image is a grayscale image without noise as a result of the pre-processing. These preprocessed images are used in classification with ML and DL. The Speeded Up Robust Features (SURF) are extracted from the grayscale image for ML-based learning. The features are restructured as visual words using the Bag of Visual Words (BoVW) method. Then, histograms are generated for each image according to the frequency of the visual word. Those histograms represent the new feature data. The histogram features are classified by four different ML methods, Decision Tree (DT), k-Nearest Neighbor (KNN), Naive Bayes (NB) and Support Vector Machine (SVM). Before using the ML methods, Bayesian Optimization (BO) method, which is one of the Hyperparameter Optimization (HO) algorithms, is applied to determine hyperparameters. In the classification process performed with four different ML algorithms, the best accuracy is achieved with the KNN algorithm as 98.09%. Resnet18, ResNet50, MobileNet, GoogLeNet, DenseNet, which are state-of-the-art CNN architectures, are used for DL-based learning. CNN models have higher accuracy than ML algorithms.

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“…The features are fused and selected to form a useful feature set for subsequent damage type identification. Color features [23]: This paper uses the statistical properties of the image samples' three color channels (red, green, and blue) to represent the image features. The mean, standard deviation, skewness, kurtosis, color entropy, and color range of the image color moments are extracted to characterize the color statistics of the pipeline image.…”

Section: Image Feature Extractionmentioning

confidence: 99%

Recognition of Corrosion State of Water Pipe Inner Wall Based on SMA-SVM under RF Feature Selection

Zhao

Zhang

et al. 2022

Coatings

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To solve the problem of low detection accuracy of water supply pipeline internal wall damage, a random forest algorithm with simplified features and a slime mold optimization support vector machine detection method was proposed. Firstly, the color statistical characteristics, gray level co-occurrence matrix, and gray level run length matrix features of the pipeline image are extracted for multi-feature fusion. The contribution of the fused features is analyzed using the feature simplified random forest algorithm, and the feature set with the strongest feature expression ability is selected for classification and recognition. The global search ability of the slime mold optimization algorithm is used to find the optimal kernel function parameters and penalty factors of the support vector machine model. Finally, the optimal parameters are applied to support the vector machine model for classification prediction. The experimental results show that the recognition accuracy of the classification model proposed in this paper reaches 94.710% on the data sets of different corrosion forms on the inner wall of the pipeline. Compared with the traditional Support Vector Machines (SVM) classification model, the SVM model based on differential pollination optimization, the SVM model based on particle swarm optimization, and the back propagation (BP) neural network classification model, it is improved by 4.786%, 3.023%, 4.030%, and 0.503% respectively.

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Feature fusion method using BoVW framework for enhancing image retrieval

Cited by 16 publications

References 22 publications

Research on defect detection method of bearing dust cover based on machine vision and multi-feature fusion algorithm

Research on defect detection method of bearing dust cover based on machine vision and multi-feature fusion algorithm

Comparative Analysis of CNN Models and Bayesian Optimization-Based Machine Learning Algorithms in Leaf Type Classification

Recognition of Corrosion State of Water Pipe Inner Wall Based on SMA-SVM under RF Feature Selection

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