Color texture classification based on proposed impulse-noise resistant color local binary patterns and significant points selection algorithm

Fekri-Ershad, Shervan; Tajeripour, Farshad

doi:10.1108/sr-07-2016-0120

Cited by 37 publications

(15 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…First of all, texture feature is a significant lowlevel feature that can describe the content or region of an image very adequately. Hence, concatenation of some novel texture descriptors such as hybrid color local binary patterns (HCLBP) [175], moment invariant features, wavelet features, local binary pattern (LBP) [176] and elongated quinary pattern (EQP) [177] with deep features can lead to a superior performance of network model. Secondly, researchers can develop entirely new CNN architecture for the analysis of cervical pap smear cells.…”

Section: Discussionmentioning

confidence: 99%

A Survey for Cervical Cytopathology Image Analysis Using Deep Learning

Rahaman

et al. 2020

IEEE Access

View full text Add to dashboard Cite

Cervical cancer is one of the most common and deadliest cancers among women. Despite that, this cancer is entirely treatable if it is detected at a precancerous stage. Pap smear test is the most extensively performed screening method for early detection of cervical cancer. However, this hand-operated screening approach suffers from a high false-positive result because of human errors. To improve the accuracy and manual screening practice, computer-aided diagnosis methods based on deep learning is developed widely to segment and classify the cervical cytology images automatically. In this survey, we provide a comprehensive study of the state of the art approaches based on deep learning for the analysis of cervical cytology images. Firstly, we introduce deep learning and its simplified architectures that have been used in this field. Secondly, we discuss the publicly available cervical cytopathology datasets and evaluation metrics for segmentation and classification tasks. Then, a thorough review of the recent development of deep learning for the segmentation and classification of cervical cytology images is presented. Finally, we investigate the existing methodology along with the most suitable techniques for the analysis of pap smear cells.

show abstract

Section: Discussionmentioning

confidence: 99%

A Survey for Cervical Cytopathology Image Analysis Using Deep Learning

Rahaman

et al. 2020

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Gabor + FuzzySVM [29] 94.84 LBPH+Haarlike + SVM [30] 91.06 Patch Based + Bayesian [31] 90.83 HCLBP + KNN [33] 93.67…”

Section: Methodsmentioning

confidence: 99%

Gender Classification in Human Face Images for Smart Phone Applications Based on Local Texture Information and Evaluated Kullback-Leibler Divergence

Fekri-Ershad¹

2019

View full text Add to dashboard Cite

One of the main steps in human identification is gender classification which can increase the identification accuracy. In many smart phone applications, human identification plays an important role in different reasons such as login permission, sign up certificates, etc. So, accurate gender classification algorithms may increase the accuracy of smart phone applications and reduce its complexity. Also, one of the benefits of gender classification algorithms is for parents to monitor the social network contacts of their child in terms of gender. Different methods have been proposed to do it accurately so far. In all methods, classification accuracy is the main challenge for researchers. But, in smart phone applications, some challenges such as rotation, gray scale variations may reduce the accuracy. In this respect, a rotation invariant approach is proposed in this paper to classify genders in human face images based on improved version of local binary patters (ILBP). Local binary pattern (LBP) is a texture descriptor, which extract local contrast and spatial structure information. Some issues such as noise sensitivity, rotation sensitivity and low discriminative features can be considered as disadvantages of the basic LBP. ILBP solves the above disadvantages using a new theory for binary patterns categorization. The proposed approach includes two stages. First of all, a feature vector is extracted for human face images based on ILBP. Next, Kullback-Leibler divergence classifier is used to classify gender. In this paper Kullback Leibler classifier is evaluated based on log likelihood ratio as distance measure. In the result part, two databases, self-collected and ICPR are used as human face database. Results are compared by different well known methods in this literature that shows the high quality of the proposed approach in terms of accuracy rate. Other main advantages of our approach are rotation invariant, low noise sensitivity, size invariant and low computational complexity. The proposed approach decreases the computational complexity of smartphone applications because of reducing the number of database comparisons. It can also improve performance of the synchronous applications in the smartphones because of memory and CPU usage reduction.

show abstract

“…Several variations of LBP have been proposed to improve the sampling scheme or computing efficiency [33, 34]. Furthermore, hybrid colour LBP (HCLBP) [35] and noise‐resistant colour LBP (Nr‐CLBP) [35] are hybrid feature extractors which use both colour and texture features jointly. Other commonly used statistical‐based approaches are SIFT [36], LPQ [37], SURF [38] etc.…”

Section: Related Workmentioning

confidence: 99%