Classification via Deep Fuzzy c-Means Clustering

Yeganejou, Mojtaba; Dick, Scott

doi:10.1109/fuzz-ieee.2018.8491461

Cited by 21 publications

(13 citation statements)

References 20 publications

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“…In order to solve the problems caused by the appearance of inner mouth components, we adopt fuzzy learning to achieve a discriminative feature representation for lip segmentation. Recent research [28][29][30] has applied fuzzy logic to machine learning fields.…”

Section: Introductionmentioning

confidence: 99%

Lip image segmentation based on a fuzzy convolutional neural network

Guan

Wang

Liew

2019

IEEE Trans. Fuzzy Syst.

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Research has shown that the human lip and its movements are a rich source of information related to speech content and speaker's identity. Lip image segmentation, as a fundamental step in many lipreading and visual speaker authentication systems, is of vital importance. Because of variations in lip color, lighting conditions and especially the complex appearance of an open mouth, accurate lip region segmentation is still a challenging task. To address this problem, this paper proposes a new fuzzy deep neural network having an architecture that integrates fuzzy units and traditional convolutional units. The convolutional units are used to extract discriminative features at different scales to provide comprehensive information for pixel-level lip segmentation. The fuzzy logic modules are employed to handle various kinds of uncertainties and to provide a more robust segmentation result. An end-to-end training scheme is then used to learn the optimal parameters for both the fuzzy and the convolutional units. A dataset containing more than 48,000 images of various speakers, under different lighting conditions, was used to evaluate lip segmentation performance. According to the experimental results, the proposed method achieves state-of-the-art performance when compared with other algorithms.

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Section: Introductionmentioning

confidence: 99%

Lip image segmentation based on a fuzzy convolutional neural network

Guan

Wang

Liew

2019

IEEE Trans. Fuzzy Syst.

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“…The advantage of using K-means is its applicability and simplicity in several fields; as a batch based algorithm, it comes with various limitation as it has poor initialization. In recent years, deep learning has been one of the major research areas; a supervised learning task that has gained satisfactory results in big data clustering [17][18][19][20]; fails to deliver the result among the raw data and it affects the accuracy. Hence several rough based or fuzzy based approach is developed for handling the uncertainty in clustering.…”

Section: Literature Surveymentioning

confidence: 99%

Big Data Clustering Using Improvised Fuzzy C-Means Clustering

Rayala¹,

Kalli²

2020

RIA

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Clustering emerged as powerful mechanism to analyze the massive data generated by modern applications; the main aim of it is to categorize the data into clusters where objects are grouped into the particular category. However, there are various challenges while clustering the big data recently. Deep Learning has been powerful paradigm for big data analysis, this requires huge number of samples for training the model, which is time consuming and expensive. This can be avoided though fuzzy approach. In this research work, we design and develop an Improvised Fuzzy C-Means (IFCM)which comprises the encoder decoder Convolutional Neural Network (CNN) model and Fuzzy C-means (FCM) technique to enhance the clustering mechanism. Encoder decoder based CNN is used for learning feature and faster computation. In general, FCM, we introduce a function which measure the distance between the cluster center and instance which helps in achieving the better clustering and later we introduce Optimized Encoder Decoder (OED) CNN model for improvising the performance and for faster computation. Further in order to evaluate the proposed mechanism, three distinctive data types namely Modified National Institute of Standards and Technology (MNIST), fashion MNIST and United States Postal Service (USPS) are used, also evaluation is carried out by considering the performance metric like Accuracy, Adjusted Rand Index (ARI) and Normalized Mutual Information (NMI). Moreover, comparative analysis is carried out on each dataset and comparative analysis shows that IFCM outperforms the existing model.

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“…Deep learning is a supervised learning task which has gained a lot of success in the task of large-scale image data clusters and classification [1], [24], [40], [53] for big data. However, deep learning models do not discuss the uncertainties amongst the raw data, affecting accuracy.…”

Section: Related Work a Fuzzy-rough C-mean Clustering (Frcm)mentioning

confidence: 99%

“…However, deep learning models do not discuss the uncertainties amongst the raw data, affecting accuracy. Therefore, numerous Fuzzy or Rough based deep learning approaches are introduced to handle this uncertainty problem for clustering and classification [1], [24], [43]- [45], [53], [54]. Deng et al [1], proposed hierarchically approaches to fuses the fuzzy logic and neural network that simultaneously leaned feature representations altogether for robust data classification.…”

Section: Related Work a Fuzzy-rough C-mean Clustering (Frcm)mentioning

confidence: 99%

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A Semi-Supervised CNN With Fuzzy Rough C-Mean for Image Classification

2019

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While deep learning (DL) has proven to be a powerful paradigm for the classification of the large-scale big image data set. Deep learning network such as CNN requires a large number of labeled samples for training the network. However, often times the labeled data are difficult, expensive, and timeconsuming. In this paper, we propose a semi-supervised approach to fused fuzzy-rough C-mean clustering with convolutional neural networks (CNNs) to knowledge learning from simultaneously intra-model and inter-model relationships forming final data representation to be classified, which can be achieved by better performance. Conception behind this is to reduce the uncertainty in terms of vagueness and indiscernibility by using fuzzy-rough C-mean clustering and specifically removing noise samples by using CNN from raw data. The framework of our proposed semi-supervised approach uses all the training data with abundant unlabeled data with few labeled data to train FRCNN model. To show the effectiveness of our model, we used four benchmark large-scale image datasets and also compared it with the state-of-the-art supervised, unsupervised, and semi-supervised learning methods for image classification. INDEX TERMS Semi-supervised learning, convolutional neural network, fuzzy rough C-mean clustering, feature learning, and image classification.

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Classification via Deep Fuzzy c-Means Clustering

Cited by 21 publications

References 20 publications

Lip image segmentation based on a fuzzy convolutional neural network

Lip image segmentation based on a fuzzy convolutional neural network

Big Data Clustering Using Improvised Fuzzy C-Means Clustering

A Semi-Supervised CNN With Fuzzy Rough C-Mean for Image Classification

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