Automatic Image Annotation Based on Hidden Markov Model and Convolutional Neural Network

2018 ◽  
Vol 08 (09) ◽  
pp. 1309-1316
Author(s):  
海蛟 徐
Keyword(s):  
Neural Network ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Convolutional Neural Network ◽  
Image Annotation ◽  
Hidden Markov ◽  
Automatic Image Annotation

scholarly journals A Method of Combining Hidden Markov Model and Convolutional Neural Network for the 5G RCS Message Filtering

2021 ◽  
Vol 11 (14) ◽  
pp. 6350
Author(s):  
Bibu Gao ◽  
Wenqiang Zhang
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Convolutional Neural Network ◽  
Hidden Markov ◽  
Short Text ◽  
Feature Extraction Algorithm ◽  
Extraction Algorithm ◽  
Message Service

As one of the 5G applications, rich communication suite (RCS), known as the next generation of Short Message Service (SMS), contains multimedia and interactive information for a better user experience. Meanwhile, the RCS industry worries that spammers may migrate their spamming misdeeds to RCS messages, the complexity of which challenges the filtering technology because each of them contains hundreds of fields with various types of data, such as texts, images and videos. Among the data, the hundreds of fields of text data contain the main content, which is adequate and more efficient for combating spam. This paper first discusses the text fields, which possibly contain spam information, then use the hidden Markov model (HMM) to weight the fields and finally use convolutional neural network (CNN) to classify the RCS messages. In the HMM step, the text fields are treated differently. The short texts of these fields are represented as feature weight sequences extracted by a feature extraction algorithm based on a probability density function. Then, the proposed HMM learns the weight sequence and produces a proper weight for each short text. Other text fields with fewer words are also weighted by the feature extraction algorithm. In the CNN step, all these feature weights first construct the RCS message matrix. The matrices of the training RCS messages are used as the CNN model inputs for learning and the matrices of testing messages are used as the trained CNN model inputs for RCS message property prediction. Four optimization technologies are introduced into the CNN classification process. Promising experiment results are achieved on the real industrial data.

scholarly journals Evaluating Convolutional Neural Network and Hidden Markov Model for Recognising Surgical Phases in Sigmoid Resection

2018 ◽  
Vol 4 (1) ◽  
pp. 415-418 ◽  
Author(s):  
Nour Aldeen Jalal ◽  
Tamer Abdulnaki Alshirbaji ◽  
Knut Möller
Keyword(s):  
Neural Network ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Convolutional Neural Network ◽  
Hidden Markov ◽  
Sigmoid Resection ◽  
Study Phase ◽  
Surgical Workflow ◽  
Surgical Workflow Analysis ◽  
Phase Recognition

AbstractSurgical workflow analysis in laparoscopic surgeries has been studied widely during last years because of its various applications. For example, optimising the schedule of operating rooms (OR) and developing a context-aware system that supports surgical team during the intervention. Surgical phase recognition has been applied to various kinds of laparoscopic procedures, mainly of type cholecystectomy. Sigmoid resection procedures are considered more complex than cholecystectomy, and they have not been extensively studied. Therefore, the focus of this work is to study phase recognition in sigmoid resection. In this paper, a convolutional neural network (CNN) architecture and Hidden Markov Model (HMM) were evaluated for performing phase recognition in sigmoid resection videos. The CNN is an extension of a pretrained model, and it was fine-tuned to perform the recognition. To consider the temporal aspect of the phase sequences, confidences obtained by the CNN were then provided into a HMM to release final classification. Experimental results show a low performance of the proposed method to recognise surgical phases in such complex procedures. Therefore, the dataset used for the evaluation was also reviewed, and statistics of each phase were generated.

scholarly journals A Hybrid Hidden Markov Model for Pipeline Leakage Detection

2021 ◽  
Vol 11 (7) ◽  
pp. 3138
Author(s):  
Mingchi Zhang ◽  
Xuemin Chen ◽  
Wei Li
Keyword(s):  
Neural Network ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Deep Neural Network ◽  
Hidden Markov ◽  
Gaussian Mixture ◽  
State Sequence ◽  
Non Linear

In this paper, a deep neural network hidden Markov model (DNN-HMM) is proposed to detect pipeline leakage location. A long pipeline is divided into several sections and the leakage occurs in different section that is defined as different state of hidden Markov model (HMM). The hybrid HMM, i.e., DNN-HMM, consists of a deep neural network (DNN) with multiple layers to exploit the non-linear data. The DNN is initialized by using a deep belief network (DBN). The DBN is a pre-trained model built by stacking top-down restricted Boltzmann machines (RBM) that compute the emission probabilities for the HMM instead of Gaussian mixture model (GMM). Two comparative studies based on different numbers of states using Gaussian mixture model-hidden Markov model (GMM-HMM) and DNN-HMM are performed. The accuracy of the testing performance between detected state sequence and actual state sequence is measured by micro F1 score. The micro F1 score approaches 0.94 for GMM-HMM method and it is close to 0.95 for DNN-HMM method when the pipeline is divided into three sections. In the experiment that divides the pipeline as five sections, the micro F1 score for GMM-HMM is 0.69, while it approaches 0.96 with DNN-HMM method. The results demonstrate that the DNN-HMM can learn a better model of non-linear data and achieve better performance compared to GMM-HMM method.

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