Object classification using a local texture descriptor and a support vector machine

Ferraz, Carolina Toledo; Gonzaga, Adilson

doi:10.1007/s11042-016-4003-0

Cited by 5 publications

(3 citation statements)

References 22 publications

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“…Ferraz and Gonzaga [ 5 ] introduced a study focused on object classification based on local texture descriptor and a support vector machine. Recently, two new texture descriptors are proposed for object detection based on the Local Mapped Pattern (LPM) approach.…”

Section: Literature Reviewmentioning

confidence: 99%

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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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Section: Literature Reviewmentioning

confidence: 99%

“…Paper Advantage Disadvantage 2017 [5] (i) High speed of processing (i) A lot of parameters required for training (ii) High accuracy (iii) Having an ability to intervene in big dataset images (ii) Network architecture complex 2018 [8] (i) Less processing times…”

Section: Yearmentioning

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

View full text Add to dashboard Cite

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“…Back-propagation neural network and SVM algorithms have been widely used for classification, identification, prediction, and detection that produce a fairly good degree of accuracy. The applications of back-propagation neural network and SVM for classification are the fruit classification, ship classification, natural gas pipeline classification, automatic text classification, cancer classification, audio sounds classification, handling binary classification, enzyme classification and object classification [6][7][8][9][10][11][12][13][14][15]. The applications of the two classifiers for identification are defect identification for simple fleshy fruits, hand writer character recognition, transcription factor binding sites identification on human genome, diagnosis of renal calculus disease, and automated speech signal analysis [16][17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

Automatic Detection of Motorcycle on the Road using Digital Image Processing

Sutikno

Wibawa²,

Saputra³

2018

SJI

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Traffic accident is one of the causes of death in the world. One of them is traffic accidents on motorcyclist not wearing helmets. To overcome this problem, several researchers have developed detection system of motorcyclist not wear helmet. This system consists of motorcycle detection and motorcyclist head detection. On motorcycle detection, accuracy still needs to be improved. For this reason, this paper proposed motorcycle detection by adding image improvement processes that are enhancing contrast and adding object positioning features.The techniques proposed in this study are divided into 3 stages of image enhancement, feature extraction, and classification. The image enhancement stage consists of enhance contrast, convert RGB image to gray scale, background subtraction, convert gray scale image to binary, closing operation, and small object removal. The features used in this paper are the features of the object area, the circumference of the object, and the location of the object, while the method for classification process using back-propagation neural network and SVM. The proposed method resulted in an accuracy of 96.97%. Error occurs in all image test data not motorcycle objects detected as motorcycle objects. This error is caused because the pixel value between the objects in the image with the background color has a level of difference is too small, so it is detected as an object not a motorcycle.

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

Srivastava

Rajitha

Agarwal

2018

Multimed Tools Appl

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Object classification using a local texture descriptor and a support vector machine

Cited by 5 publications

References 22 publications

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

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

Automatic Detection of Motorcycle on the Road using Digital Image Processing

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

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