Image Classification and Detection of Insulators using Bag of Visual Words and Speeded up Robust Features

Electricalsubstation online monitoring in computer vision technology is based on image processingalgorithm to perform visual analysis.This paperpresents classification of ceramicand glass insulators through Bag of Visual Words and detection of these insulators by Point Feature Matching.The training image datasets are used for categorization by forming a visual vocabularywhile a new unlabeled image from testing image dataset is classify using nearest neighbor classification method for features descriptor. For detection we use Speeded up Robust Features for detecting position of insulator present in cluttered scene image. Matching process is done between test and reference image and decision is made based on similar features. Weconducted experiment on insulators to verify the superiority of our proposed method.The proposed method can be used in security, surveillance and inspection system.

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Discrimination of Common Ragweed (Ambrosia artemisiifolia) and Mugwort (Artemisia vulgaris) Based on Bag of Visual Words Model

Weed Technology ◽

10.1614/wt-d-16-00068.1 ◽

2017 ◽

Vol 31 (2) ◽

pp. 310-319 ◽

Cited By ~ 2

Author(s):

Anton Ustyuzhanin ◽

Karl-Heinz Dammer ◽

Antje Giebel ◽

Cornelia Weltzien ◽

Michael Schirrmann

Keyword(s):

Plant Species ◽

Ambrosia Artemisiifolia ◽

Training Data ◽

Support Vector ◽

Identification System ◽

Bag Of Visual Words ◽

Common Ragweed ◽

Visual Words ◽

Grid Sampling ◽

Speeded Up Robust Features

Common ragweed is a plant species causing allergic and asthmatic symptoms in humans. To control its propagation, an early identification system is needed. However, due to its similar appearance with mugwort, proper differentiation between these two weed species is important. Therefore, we propose a method to discriminate common ragweed and mugwort leaves based on digital images using bag of visual words (BoVW). BoVW is an object-based image classification that has gained acceptance in many areas of science. We compared speeded-up robust features (SURF) and grid sampling for keypoint selection. The image vocabulary was built using K-means clustering. The image classifier was trained using support vector machines. To check the robustness of the classifier, specific model runs were conducted with and without damaged leaves in the trainings dataset. The results showed that the BoVW model allows the discrimination between common ragweed and mugwort leaves with high accuracy. Based on SURF keypoints with 50% of 788 images in total as training data, we achieved a 100% correct recognition of the two plant species. The grid sampling resulted in slightly less recognition accuracy (98 to 99%). In addition, the classification based on SURF was up to 31 times faster.

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Emotion Recognition from Speech Using the Bag-of-Visual Words on Audio Segment Spectrograms

Technologies ◽

10.3390/technologies7010020 ◽

2019 ◽

Vol 7 (1) ◽

pp. 20 ◽

Cited By ~ 3

Author(s):

Evaggelos Spyrou ◽

Rozalia Nikopoulou ◽

Ioannis Vernikos ◽

Phivos Mylonas

Keyword(s):

Computer Vision ◽

Emotion Recognition ◽

Affective State ◽

Real Life ◽

Support Vector ◽

Bag Of Visual Words ◽

Educational Training ◽

Visual Words ◽

Speeded Up Robust Features ◽

Digital World

It is noteworthy nowadays that monitoring and understanding a human’s emotional state plays a key role in the current and forthcoming computational technologies. On the other hand, this monitoring and analysis should be as unobtrusive as possible, since in our era the digital world has been smoothly adopted in everyday life activities. In this framework and within the domain of assessing humans’ affective state during their educational training, the most popular way to go is to use sensory equipment that would allow their observing without involving any kind of direct contact. Thus, in this work, we focus on human emotion recognition from audio stimuli (i.e., human speech) using a novel approach based on a computer vision inspired methodology, namely the bag-of-visual words method, applied on several audio segment spectrograms. The latter are considered to be the visual representation of the considered audio segment and may be analyzed by exploiting well-known traditional computer vision techniques, such as construction of a visual vocabulary, extraction of speeded-up robust features (SURF) features, quantization into a set of visual words, and image histogram construction. As a last step, support vector machines (SVM) classifiers are trained based on the aforementioned information. Finally, to further generalize the herein proposed approach, we utilize publicly available datasets from several human languages to perform cross-language experiments, both in terms of actor-created and real-life ones.

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Commodity Image Classification Based on Improved Bag-of-Visual-Words Model

Complexity ◽

10.1155/2021/5556899 ◽

2021 ◽

Vol 2021 ◽

pp. 1-10

Author(s):

Huadong Sun ◽

Xu Zhang ◽

Xiaowei Han ◽

Xuesong Jin ◽

Zhijie Zhao

Keyword(s):

Feature Extraction ◽

Image Classification ◽

Local Feature ◽

Image Feature ◽

Bag Of Visual Words ◽

Shape Information ◽

Image Feature Extraction ◽

Visual Words ◽

Speeded Up Robust Features ◽

Rectangular Pattern

With the increasing scale of e-commerce, the complexity of image content makes commodity image classification face great challenges. Image feature extraction often determines the quality of the final classification results. At present, the image feature extraction part mainly includes the underlying visual feature and the intermediate semantic feature. The intermediate semantics of the image acts as a bridge between the underlying features and the advanced semantics of the image, which can make up for the semantic gap to a certain extent and has strong robustness. As a typical intermediate semantic representation method, the bag-of-visual-words (BoVW) model has received extensive attention in image classification. However, the traditional BoVW model loses the location information of local features, and its local feature descriptors mainly focus on the texture shape information of local regions but lack the expression of color information. Therefore, in this paper, the improved bag-of-visual-words model is presented, which contains three aspects of improvement: (1) multiscale local region extraction; (2) local feature description by speeded up robust features (SURF) and color vector angle histogram (CVAH); and (3) diagonal concentric rectangular pattern. Experimental results show that the three aspects of improvement to the BoVW model are complementary, while compared with the traditional BoVW and the BoVW adopting SURF + SPM, the classification accuracy of the improved BoVW is increased by 3.60% and 2.33%, respectively.

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Bag-of-Visual-Words for Cattle Identification from Muzzle Print Images

Applied Sciences ◽

10.3390/app9224914 ◽

2019 ◽

Vol 9 (22) ◽

pp. 4914 ◽

Cited By ~ 1

Author(s):

Ali Ismail Awad ◽

M. Hassaballah

Keyword(s):

Large Scale ◽

Evaluation Criteria ◽

Variable Number ◽

Identification Accuracy ◽

Bag Of Visual Words ◽

Visual Words ◽

Speeded Up Robust Features ◽

Local Invariant ◽

Invariant Features ◽

Local Invariant Features

Cattle, buffalo and cow identification plays an influential role in cattle traceability from birth to slaughter, understanding disease trajectories and large-scale cattle ownership management. Muzzle print images are considered discriminating cattle biometric identifiers for biometric-based cattle identification and traceability. This paper presents an exploration of the performance of the bag-of-visual-words (BoVW) approach in cattle identification using local invariant features extracted from a database of muzzle print images. Two local invariant feature detectors—namely, speeded-up robust features (SURF) and maximally stable extremal regions (MSER)—are used as feature extraction engines in the BoVW model. The performance evaluation criteria include several factors, namely, the identification accuracy, processing time and the number of features. The experimental work measures the performance of the BoVW model under a variable number of input muzzle print images in the training, validation, and testing phases. The identification accuracy values when utilizing the SURF feature detector and descriptor were 75%, 83%, 91%, and 93% for when 30%, 45%, 60%, and 75% of the database was used in the training phase, respectively. However, using MSER as a points-of-interest detector combined with the SURF descriptor achieved accuracies of 52%, 60%, 67%, and 67%, respectively, when applying the same training sizes. The research findings have proven the feasibility of deploying the BoVW paradigm in cattle identification using local invariant features extracted from muzzle print images.

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