Image classification using SURF and bag of LBP features constructed by clustering with fixed centers

Srivastava, Divya; Rajitha, B.; Agarwal, Suneeta

doi:10.1007/s11042-018-6793-8

Cited by 19 publications

(11 citation statements)

References 20 publications

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“…In this study, two texture features, namely local binary pattern (LBP) 32 and Haralick features, 33 are used. These features are useful in image classification and are widely utilized in previous studies 34 . Texture features consist of the information on the spatial arrangement of color and intensities in a region of a dermoscopy image that can be helpful in melanoma detection 35 …”

Section: Methodsmentioning

confidence: 99%

Automatic skin cancer detection in dermoscopy images by combining convolutional neural networks and texture features

Alizadeh

Mahloojifar

2020

Int J Imaging Syst Tech

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Melanoma is one of the most dangerous types of skin cancer that its early detection can save patients' lives. Computer‐aided methods can be used for this early detection with acceptable performance. In this study, a system is proposed to detect melanoma automatically using an ensemble approach, including convolutional neural networks (CNNs) and image texture feature extraction. Two CNN models, a proposed network and the VGG‐19, were employed to classify images in the CNN phase. Furthermore, texture features were extracted, and their dimension was reduced using kernel principal component analysis (kPCA) to improve the classification performance in the feature extraction‐based phase. The results of each step were then combined to obtain the final diagnosis. The proposed method was evaluated on three databases, that is, ISIC 2016, ISIC 2019, and PH2. The accuracy, average precision, sensitivity, and specificity of the proposed method on the ISIC 2016 dataset were 85.2%, 66%, 52%, and 93.4%, respectively. These evaluation metrics for the ISIC 2019 database were obtained equal to 96.7%, 95.1%, 96.3%, and 97.1%, respectively. Furthermore, the accuracy, sensitivity, and specificity of the proposed method on the PH2 dataset were 97.5%, 100%, and 96.88%, respectively. According to the experimental results, the ensemble method improves the evaluation metrics compared to each phase separately. Besides, the proposed approach can increase the performance of melanoma detection, compared to previous studies.

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Section: Methodsmentioning

confidence: 99%

Automatic skin cancer detection in dermoscopy images by combining convolutional neural networks and texture features

Alizadeh

Mahloojifar

2020

Int J Imaging Syst Tech

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“…The challenge was to apply CNN with device having a limited memory, and the result gives 85.9% accuracy using CIFAR-10 dataset with memory allocation of 2 GB. The limitation of this method is same as Srivastava et al [ 6 ] research which has a difficulty to train through a big dataset. Dhouibi [ 9 ] published a paper-entitled optimization of the CNN model for image classification.…”

Section: Literature Reviewmentioning

confidence: 99%

Image Classification Based on Light Convolutional Neural Network Using Pulse Couple Neural Network

Rafidison

Ramafiarisona

Randriamitantsoa

et al. 2023

Computational Intelligence and Neuroscience

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Recently, most image classification studies solicit the intervention of convolutional neural networks because these DL-based classification methods generally outperform other methodologies with higher accuracy. However, this type of deep learning networks require many parameters and have a complex structure with multiple convolutional and pooling layers depending on the objective. These layers compute a large volume of data and it may impact the processing time and the performance. Therefore, this paper proposes a new method of image classification based on the light convolutional neural network. It consists of replacing the feature extraction layers of standard convolutional neural network with a single pulse coupled neural network by introducing the notion of foveation. This module provides the feature map of input image and the data compression using Discrete Wavelet Transform which is an optional step depending on the information quantity of this signature. The fully connected neural network, which has six hidden layers, classifies the image. With this technique, the computation time is reduced, and the network architecture is identical and simple independent of the type of dataset. The number of parameter is less than that in current research. The proposed method was validated with different dataset such as Caltech-101, Caltech-256, CIFAR-10, CIFAR-100, and ImageNet, and the accuracy reaches 92%, 90%, 99%, 94%, and 91%, respectively, which are better than the previous related works.

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“…Based on the bag of feature algorithm (BoF) [32][33][34], where the main idea is to quantify the disordered set of local descriptors, we use another widely used color space, the so-called "hue, saturation, value (HSV)" color space, instead of the "red, green, blue (RGB)" color space to sharpen color images. We divide the HSV color space into 15 color categories, including three achromatic colors (black, white, gray) and twelve colors (blue, green, yellow, etc.).…”

Section: Hand-crafted Feature Extractionmentioning

confidence: 99%

Person Re-Identification by Low-Dimensional Features and Metric Learning

Bian

2021

Future Internet

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Person re-identification (Re-ID) has attracted attention due to its wide range of applications. Most recent studies have focused on the extraction of deep features, while ignoring color features that can remain stable, even for illumination variations and the variation in person pose. There are also few studies that combine the powerful learning capabilities of deep learning with color features. Therefore, we hope to use the advantages of both to design a model with low computational resource consumption and excellent performance to solve the task of person re-identification. In this paper, we designed a color feature containing relative spatial information, namely the color feature with spatial information. Then, bidirectional long short-term memory (BLSTM) networks with an attention mechanism are used to obtain the contextual relationship contained in the hand-crafted color features. Finally, experiments demonstrate that the proposed model can improve the recognition performance compared with traditional methods. At the same time, hand-crafted features based on human prior knowledge not only reduce computational consumption compared with deep learning methods but also make the model more interpretable.

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Image classification using SURF and bag of LBP features constructed by clustering with fixed centers

Cited by 19 publications

References 20 publications

Automatic skin cancer detection in dermoscopy images by combining convolutional neural networks and texture features

Automatic skin cancer detection in dermoscopy images by combining convolutional neural networks and texture features

Image Classification Based on Light Convolutional Neural Network Using Pulse Couple Neural Network

Person Re-Identification by Low-Dimensional Features and Metric Learning

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