Study and Analysis of Back-Propagation Approach in Artificial Neural Network Using HOG Descriptor for Real-Time Object Classification

Gupta, Vaibhav; Sunita, Sunita; Singh, Jai

doi:10.1007/978-981-13-0589-4_5

Cited by 8 publications

(3 citation statements)

References 4 publications

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“…The Delta rule is used based on the least-squares error in BPANN. Training process with a set of data consisting of specified input and output parameters based on hidden layer and weighting layer adjustment [22]. This iteration works by updating the weights and reducing the residuals of the neural network output (the difference between the calculated output and the actual output) and has two main feedback and propagation steps.…”

Section: Bpannmentioning

confidence: 99%

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Deep learning of total electron content

Sorkhabi

2021

SN Appl. Sci.

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One of the most notable errors in the global navigation satellite system (GNSS) is the ionospheric delay due to the total electron content (TEC). TEC is the number of electrons in the ionosphere in the signal path from the satellite to the receiver, which fluctuates with time and location. This error is one of the major problems in single-frequency (SF) GPS receivers. One way to eliminate this error is to use dual-frequency. Users of SF receivers should either use estimation models or local models to reduce this error. In this study, deep learning of artificial neural networks (ANN) was used to estimate TEC for SF users. For this purpose, the ionosphere as a single-layer model (assuming that all free electrons in the ionosphere are in this thin layer) is locally modeled by the code observation method. Linear combination has been used by selecting 24 permanent GNSS stations in the northwest of Iran. TEC was modeled independently of the geometry between the satellite and the receiver, called L4. This modeling was used to train the error ANN with two 5-day periods of high and low solar and geomagnetic activity range with a hyperbolic tangential sigmoid activation function. The results show that the proposed method is capable of eliminating ionosphere error with an average accuracy of 90%. The international reference ionosphere 2016 (IRI2016) is used for the verification, which has a 96% significance correlation with estimated TEC.

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Section: Bpannmentioning

confidence: 99%

“…Seventy percent of the data were used for training, 15% for testing, and 15% for validation. Further details on the BPANN learning process can be found in Bishop [20], Yang et al [25] and Gupta and Singh [22].…”

Section: Bpannmentioning

confidence: 99%

Deep learning of total electron content

Sorkhabi

2021

SN Appl. Sci.

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“…Although in the past few decades, the conventional approaches, such as Hand-Crafted Features (HCF), were used, as the time passed, however, the objects and their backgrounds became more confusing, thereby restricting their use. Handcrafted features included Histogram of Oriented Graph (HOG) [6], geometric features [7], Scale Invariant Feature Transformation (SIFT) [8], Difference of Gaussian (DoG) [9], Speeded-Up Robust Features (SURF) [10], and texture features (HARLICK) [11]. Recent techniques, in contrast, proposed to exploit a hybrid set of features to get a better representation of an object [12].…”

Section: Introductionmentioning

confidence: 99%

A Sustainable Deep Learning Framework for Object Recognition Using Multi-Layers Deep Features Fusion and Selection

et al. 2020

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With an overwhelming increase in the demand of autonomous systems, especially in the applications related to intelligent robotics and visual surveillance, come stringent accuracy requirements for complex object recognition. A system that maintains its performance against a change in the object’s nature is said to be sustainable and it has become a major area of research for the computer vision research community in the past few years. In this work, we present a sustainable deep learning architecture, which utilizes multi-layer deep features fusion and selection, for accurate object classification. The proposed approach comprises three steps: (1) By utilizing two deep learning architectures, Very Deep Convolutional Networks for Large-Scale Image Recognition and Inception V3, it extracts features based on transfer learning, (2) Fusion of all the extracted feature vectors is performed by means of a parallel maximum covariance approach, and (3) The best features are selected using Multi Logistic Regression controlled Entropy-Variances method. For verification of the robust selected features, the Ensemble Learning method named Subspace Discriminant Analysis is utilized as a fitness function. The experimental process is conducted using four publicly available datasets, including Caltech-101, Birds database, Butterflies database and CIFAR-100, and a ten-fold validation process which yields the best accuracies of 95.5%, 100%, 98%, and 68.80% for the datasets respectively. Based on the detailed statistical analysis and comparison with the existing methods, the proposed selection method gives significantly more accuracy. Moreover, the computational time of the proposed selection method is better for real-time implementation.

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