PolSAR image classification based on complex-valued convolutional neural network and Markov random field

2019 ◽  
Author(s):  
Xianxiang Qin ◽  
Wangsheng Yu ◽  
Peng Wang ◽  
Tianping Chen ◽  
Huanxin Zou
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Random Field ◽  
Image Classification ◽  
Markov Random Field ◽  
Markov Random ◽  
Complex Valued

scholarly journals Image Crowd Counting Using Convolutional Neural Network and Markov Random Field

2017 ◽  
Vol 21 (4) ◽  
pp. 632-638 ◽  
Author(s):  
Kang Han ◽  
◽  
Wanggen Wan ◽  
Haiyan Yao ◽  
Li Hou
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Random Field ◽  
Markov Random Field ◽  
Visible Image ◽  
Crowd Counting ◽  
Markov Random ◽  
Local Patch ◽  
Dense Crowd ◽  
Patch Image

In this paper, we propose a method called Convolutional Neural Network-Markov Random Field (CNN-MRF) to estimate the crowd count in a still image. We first divide the dense crowd visible image into overlapping patches and then use a deep convolutional neural network to extract features from each patch image, followed by a fully connected neural network to regress the local patch crowd count. Since the local patches have overlapping portions, the crowd count of the adjacent patches has a high correlation. We use this correlation and the Markov random field to smooth the counting results of the local patches. Experiments show that our approach significantly outperforms the state-of-the-art methods on UCF and Shanghaitech crowd counting datasets.

scholarly journals Combining Convolutional Neural Network and Markov Random Field for Semantic Image Retrieval

2018 ◽  
Vol 2018 ◽  
pp. 1-11
Author(s):  
Haijiao Xu ◽  
Changqin Huang ◽  
Xiaodi Huang ◽  
Chunyan Xu ◽  
Muxiong Huang
Keyword(s):  
Neural Network ◽  
Image Retrieval ◽  
Convolutional Neural Network ◽  
Random Field ◽  
Markov Random Field ◽  
Concept Detection ◽  
Semantic Image Retrieval ◽  
Semantic Correlation ◽  
Markov Random ◽  
Fully Connected

With the rapidly growing number of images over the Internet, efficient scalable semantic image retrieval becomes increasingly important. This paper presents a novel approach for semantic image retrieval by combining Convolutional Neural Network (CNN) and Markov Random Field (MRF). As a key step, image concept detection, that is, automatically recognizing multiple semantic concepts in an unlabeled image, plays an important role in semantic image retrieval. Unlike previous work that uses single-concept classifiers one by one, we detect semantic multiconcept by using a multiconcept scene classifier. In other words, our approach takes multiple concepts as a holistic scene for multiconcept scene learning. Specifically, we first train a CNN as a concept classifier, which further includes two types of classifiers: a single-concept fully connected classifier that is best suited to single-concept detection and a multiconcept scene fully connected classifier that is good for holistic scene detection. Then we propose an MRF-based late fusion approach that is able to effectively learn the semantic correlation between the single-concept classifier and multiconcept scene classifier. Finally, the semantic correlation among the subconcepts of images is cought to further improve detection precision. In order to investigate the feasibility and effectiveness of our proposed approach, we conduct comprehensive experiments on two publicly available image databases. The results show that our proposed approach outperforms several state-of-the-art approaches.

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