Wavelet Based Histogram of Oriented Gradients Feature Descriptors for Classification of Partially Occluded Objects

Singh, Ajay Kumar; Shukla, V. P.; Tiwari, Shamik; Biradar, S. R.

doi:10.5815/ijisa.2015.03.07

Cited by 8 publications

(4 citation statements)

References 17 publications

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“…The simulation results of the proposed model are compared with the existing models, such as SIFT, HOG, Wavelet based histogram of oriented gradients (WHOG) and K-SIFT models. The previous work with the comparative models, such as SIFT (Lenc and Král, 2015), HOG (Chen et al , 2014), WHOG (Singh et al , 2015) and K-SIFT (Bindu and Manjunathachary, 2017), has the same simulation parameters as the proposed work. Hence, these models are taken for comparison.…”

Section: Resultsmentioning

confidence: 99%

Hybrid feature descriptor and probabilistic neuro-fuzzy system for face recognition

Bindu

Manjunathachari

2018

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Purpose This paper aims to develop the Hybrid feature descriptor and probabilistic neuro-fuzzy system for attaining the high accuracy in face recognition system. In recent days, facial recognition (FR) systems play a vital part in several applications such as surveillance, access control and image understanding. Accordingly, various face recognition methods have been developed in the literature, but the applicability of these algorithms is restricted because of unsatisfied accuracy. So, the improvement of face recognition is significantly important for the current trend. Design/methodology/approach This paper proposes a face recognition system through feature extraction and classification. The proposed model extracts the local and the global feature of the image. The local features of the image are extracted using the kernel based scale invariant feature transform (K-SIFT) model and the global features are extracted using the proposed m-Co-HOG model. (Co-HOG: co-occurrence histograms of oriented gradients) The proposed m-Co-HOG model has the properties of the Co-HOG algorithm. The feature vector database contains combined local and the global feature vectors derived using the K-SIFT model and the proposed m-Co-HOG algorithm. This paper proposes a probabilistic neuro-fuzzy classifier system for the finding the identity of the person from the extracted feature vector database. Findings The face images required for the simulation of the proposed work are taken from the CVL database. The simulation considers a total of 114 persons form the CVL database. From the results, it is evident that the proposed model has outperformed the existing models with an improved accuracy of 0.98. The false acceptance rate (FAR) and false rejection rate (FRR) values of the proposed model have a low value of 0.01. Originality/value This paper proposes a face recognition system with proposed m-Co-HOG vector and the hybrid neuro-fuzzy classifier. Feature extraction was based on the proposed m-Co-HOG vector for extracting the global features and the existing K-SIFT model for extracting the local features from the face images. The proposed m-Co-HOG vector utilizes the existing Co-HOG model for feature extraction, along with a new color gradient decomposition method. The major advantage of the proposed m-Co-HOG vector is that it utilizes the color features of the image along with other features during the histogram operation.

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Section: Resultsmentioning

confidence: 99%

Hybrid feature descriptor and probabilistic neuro-fuzzy system for face recognition

Bindu

Manjunathachari

2018

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“…HH, HL, LH, LL which contain diagonal contents, vertical contents, horizontal contents, and approximate contents respectively. The approximation component (LL) is used for decomposing the image in the next level [17]. Fig.4 represents the schema of DWT decomposition that is done recursively for 3 levels of decomposition.…”

Section: Discrete Wavelet Transformmentioning

confidence: 99%

Brain Diagnoses Detection Using Whale Optimization Algorithm Based on Ensemble Learning Classifier

Fouad¹,

Moftah²,

Hefny³

2020

IJIES

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Brain cancer importance emanates from the importance of the brain as an organ and its functions. It has a great effect on the whole human body. Identification brain cancer according to its type, it refers to a multiclass classification problem in the machine learning world. In the real-world, object detection and classification face numerous challenges. The object has a large variation in appearances. In this research, a Haar Discrete Wavelet transforms hybrid with the Histogram of Oriented Gradients (HDWT-HOG) features descriptors are proposed by the local gradients in MR image as shape information. The whale optimization algorithm (WOA) plays a great role to reduce the numbers of HOG and Harr features from 38,640 to 120 features only which are less than .01% from all features. This reduction doesn't affect the system performance but it saves time in the classification phase. The test image is matched with its learned class by performing a Bagging ensemble learning classifier. Bagging achieves 96.4% in average accuracy but when Boosting is used, it achieves 95.8%.

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“…At present, feature extraction methods are divided into global feature method and part feature method [1] . Histograms of gradient direction is a kind of excellent method of local feature description [2][3] , the classification ability is stronger than LBP and Gabor wavelet [4] and histograms of gradient direction has better robustness in light, scale and direction [5][6] .HOG can well describe the edge contour information of images, but [7][8] ignores the spatial arrangement and structural change information of the local features of images. Tong Ying [9] proposed a spatial multi-scale HOG feature, the image layer thinning region in different scale, multi-scale HOG image feature extraction, and applied to facial expression recognition, achieved good recognition effect.…”

Section: Introductionmentioning

confidence: 99%

Face Recognition Based on Multi-directional Histograms of Orthogonal Oriented Gradient

Wang¹,

Tang²,

Li³

et al. 2018

dtcse

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In order to solve the problem of the poor recognition effect of traditional face recognition methods under pose, facial expression and illumination, a face recognition algorithm based on Multi-directional histograms of orthogonal oriented gradient is proposed. Firstly, the gradient characteristics of eight different directions, are extracted by using improved gradient operator. Secondly, counting the histogram feature of gradient direction in each direction and the Hog feature of eight directional orthogonal gradients, in order to get the overall image characteristics, is connected together. At last, overall image characteristics will be classified by nearest neighborhood classifier. The experimental result, which is tested in face library of Yale, ORL, CAS-PEAL-R1, shows that the proposed algorithm can improve the recognition rate effectively and has excellent robustness to the illumination and expression.

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Wavelet Based Histogram of Oriented Gradients Feature Descriptors for Classification of Partially Occluded Objects

Cited by 8 publications

References 17 publications

Hybrid feature descriptor and probabilistic neuro-fuzzy system for face recognition

Hybrid feature descriptor and probabilistic neuro-fuzzy system for face recognition

Brain Diagnoses Detection Using Whale Optimization Algorithm Based on Ensemble Learning Classifier

Face Recognition Based on Multi-directional Histograms of Orthogonal Oriented Gradient

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