Robust appearance-based object recognition using a fully connected Markov random field

2003 ◽  
Author(s):  
B. Caputo ◽  
S. Bouattour ◽  
H. Niemann
Keyword(s):  
Object Recognition ◽  
Random Field ◽  
Markov Random Field ◽  
Markov Random ◽  
Fully Connected

scholarly journals 3D augmented Markov random field for object recognition

2010 ◽  
Author(s):  
Wei Yu ◽  
Ahmed Bilal Ashraf ◽  
Yao-Jen Chang ◽  
Congcong Li ◽  
Tsuhan Chen
Keyword(s):  
Object Recognition ◽  
Random Field ◽  
Markov Random Field ◽  
Markov Random

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.

Fusion Moves for Markov Random Field Optimization

2010 ◽  
Vol 32 (8) ◽  
pp. 1392-1405 ◽  
Author(s):  
Victor Lempitsky ◽  
Carsten Rother ◽  
Stefan Roth ◽  
Andrew Blake
Keyword(s):  
Random Field ◽  
Markov Random Field ◽  
Markov Random
Sign in / Sign up

Export Citation Format

Share Document