Scene image classification based on visual words concatenation of local and global features

Shrinivasa, S R; Prabhakar, C. J.

doi:10.1007/s11042-021-11354-5

Cited by 10 publications

(6 citation statements)

References 32 publications

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“…For the sake of accurate results, instead of using a global feature, local feature extraction is used to enable more efficient face recognition. Local features are considered more robust and leverage the recognition accuracy when compared to global features [61][62][63]. Therefore, in order to increase the robustness of recognition accuracy, the face image is partitioned into blocks with a block size of B y size × B x size .…”

Section: Methodsmentioning

confidence: 99%

Face Recognition Algorithm Based on Fast Computation of Orthogonal Moments

et al. 2022

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Face recognition is required in various applications, and major progress has been witnessed in this area. Many face recognition algorithms have been proposed thus far; however, achieving high recognition accuracy and low execution time remains a challenge. In this work, a new scheme for face recognition is presented using hybrid orthogonal polynomials to extract features. The embedded image kernel technique is used to decrease the complexity of feature extraction, then a support vector machine is adopted to classify these features. Moreover, a fast-overlapping block processing algorithm for feature extraction is used to reduce the computation time. Extensive evaluation of the proposed method was carried out on two different face image datasets, ORL and FEI. Different state-of-the-art face recognition methods were compared with the proposed method in order to evaluate its accuracy. We demonstrate that the proposed method achieves the highest recognition rate in different considered scenarios. Based on the obtained results, it can be seen that the proposed method is robust against noise and significantly outperforms previous approaches in terms of speed.

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Section: Methodsmentioning

confidence: 99%

Face Recognition Algorithm Based on Fast Computation of Orthogonal Moments

et al. 2022

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“…Because of its rapid computation, it is recommended over some other feature descriptors like SIFT. The SURF detector uses the Hessian matrix to select points of interest and a neighborhood descriptor to describe the intensity distribution of pixels with their neighboring points of interest 36,37 . These extracted points are stored in a feature vector and quantized using a K‐means clustering algorithm to generate the codebooks (histograms) (see Figure 2).…”

Section: Methodsmentioning

confidence: 99%

“…The SURF detector uses the Hessian matrix to select points of interest and a neighborhood descriptor to describe the intensity distribution of pixels with their neighboring points of interest. 36,37 These extracted points are stored in a feature vector and quantized using a K-means clustering algorithm to generate the codebooks (histograms) (see Figure 2). Recently, the BoF model was adopted with the goal of detecting abnormal MRI brains.…”

Section: Bag Of Featurementioning

confidence: 99%

Multimodal magnetic resonance imaging for Alzheimer's disease diagnosis using hybrid features extraction and ensemble support vector machines

Houria

Belkhamsa

Cherfa

et al. 2022

Int J Imaging Syst Tech

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Magnetic resonance imaging (MRI) is increasingly used in the diagnosis of Alzheimer's disease (AD) in order to identify abnormalities in the brain. Indeed, cortical atrophy, a powerful biomarker for AD, can be detected using structural MRI (sMRI), but it cannot detect impairment in the integrity of the white matter (WM) preceding cortical atrophy. The early detection of these changes is made possible by the novel MRI modality known as diffusion tensor imaging (DTI). In this study, we integrate DTI and sMRI as complementary imaging modalities for the early detection of AD in order to create an effective computer-assisted diagnosis tool. The fused Bag-of-Features (BoF) with Speeded-Up Robust Features (SURF) and modified AlexNet convolutional neural network (CNN) are utilized to extract local and deep features. This is applied to DTI scalar metrics (fractional anisotropy and diffusivity metric) and segmented gray matter images from T1-weighted MRI images. Then, the classification of local unimodal and deep multimodal features is first performed using support vector machine (SVM) classifiers. Then, the majority voting technique is adopted to predict the final decision from the ensemble SVMs.The study is directed toward the classification of AD versus mild cognitive impairment (MCI) versus cognitively normal (CN) subjects. Our proposed method achieved an accuracy of 98.42% and demonstrated the robustness of multimodality imaging fusion.

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“…In addition, because of the complex and diverse characteristics of the features themselves, the satellite will be affected by the background, lighting, scale, and other imaging conditions in the process of photography. Therefore, two types of feature confusion problems arose in RSSOR: scene objectives with similar semantic categories probably share different visual variability, and scene images of different semantic categories may also have certain similarities [16]. To reduce the impact of these two problems, many researchers have tried to use an attentional mechanism (AM) [17].…”

Section: Introductionmentioning

confidence: 99%

CRABR-Net: A Contextual Relational Attention-Based Recognition Network for Remote Sensing Scene Objective

Guo,

Jiang,

Gao

et al. 2023

Sensors

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Remote sensing scene objective recognition (RSSOR) plays a serious application value in both military and civilian fields. Convolutional neural networks (CNNs) have greatly enhanced the improvement of intelligent objective recognition technology for remote sensing scenes, but most of the methods using CNN for high-resolution RSSOR either use only the feature map of the last layer or directly fuse the feature maps from various layers in the “summation” way, which not only ignores the favorable relationship information between adjacent layers but also leads to redundancy and loss of feature map, which hinders the improvement of recognition accuracy. In this study, a contextual, relational attention-based recognition network (CRABR-Net) was presented, which extracts different convolutional feature maps from CNN, focuses important feature content by using a simple, parameter-free attention module (SimAM), fuses the adjacent feature maps by using the complementary relationship feature map calculation, improves the feature learning ability by using the enhanced relationship feature map calculation, and finally uses the concatenated feature maps from different layers for RSSOR. Experimental results show that CRABR-Net exploits the relationship between the different CNN layers to improve recognition performance, achieves better results compared to several state-of-the-art algorithms, and the average accuracy on AID, UC-Merced, and RSSCN7 can be up to 96.46%, 99.20%, and 95.43% with generic training ratios.

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Scene image classification based on visual words concatenation of local and global features

Cited by 10 publications

References 32 publications

Face Recognition Algorithm Based on Fast Computation of Orthogonal Moments

Face Recognition Algorithm Based on Fast Computation of Orthogonal Moments

Multimodal magnetic resonance imaging for Alzheimer's disease diagnosis using hybrid features extraction and ensemble support vector machines

CRABR-Net: A Contextual Relational Attention-Based Recognition Network for Remote Sensing Scene Objective

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