A mutual information based face clustering algorithm for movie content analysis

2011 ◽  
Vol 29 (10) ◽  
pp. 693-705 ◽  
Author(s):  
N. Vretos ◽  
V. Solachidis ◽  
I. Pitas
Keyword(s):  
Content Analysis ◽  
Mutual Information ◽  
Clustering Algorithm ◽  
Face Clustering ◽  
Movie Content

Movie Content Analysis, Indexing and Skimming Via Multimodal Information

Video Mining ◽  
2003 ◽  
pp. 123-154 ◽  
Author(s):  
Ying Li ◽  
Shrikanth Narayanan ◽  
C.-C. Jay Kuo
Keyword(s):  
Content Analysis ◽  
Movie Content ◽  
Multimodal Information

scholarly journals Simple primitive recognition via hierarchical face clustering

2020 ◽  
Vol 6 (4) ◽  
pp. 431-443
Author(s):  
Xiaolong Yang ◽  
Xiaohong Jia
Keyword(s):  
Hierarchical Clustering ◽  
Test Data ◽  
Efficient Algorithm ◽  
Clustering Algorithm ◽  
State Of The Art ◽  
Experimental Results ◽  
Face Clustering ◽  
Wide Range ◽  
Hierarchical Clustering Algorithm ◽  
Bottom To Top

AbstractWe present a simple yet efficient algorithm for recognizing simple quadric primitives (plane, sphere, cylinder, cone) from triangular meshes. Our approach is an improved version of a previous hierarchical clustering algorithm, which performs pairwise clustering of triangle patches from bottom to top. The key contributions of our approach include a strategy for priority and fidelity consideration of the detected primitives, and a scheme for boundary smoothness between adjacent clusters. Experimental results demonstrate that the proposed method produces qualitatively and quantitatively better results than representative state-of-the-art methods on a wide range of test data.

Modified Global K-Means Clustering Algorithm Using Mutual Information

2013 ◽  
Vol 19 (1) ◽  
pp. 212-215
Author(s):  
Chang-Woo Seo ◽  
Bo Kyung Cha ◽  
Ryun Kyung Kim ◽  
Sungchae Jeon ◽  
Young Huh ◽  
...  
Keyword(s):  
Mutual Information ◽  
Clustering Algorithm

scholarly journals Developing a Vaccine Informatics to Identify Message Frames Used in Vaccine Debates on Social Media: Combining Automatic Tweet Classification and Clustering Machine-Learning Algorithms with Qualitative Content Analysis (Preprint)

2020 ◽  
Author(s):  
Young Argyris ◽  
Kafui Monu ◽  
Pang-Ning Tan ◽  
Colton Aarts ◽  
Fan Jiang ◽  
...  
Keyword(s):  
Social Media ◽  
Content Analysis ◽  
Clustering Algorithm ◽  
Qualitative Content Analysis ◽  
Message Framing ◽  
Machine Learning Algorithms ◽  
Vaccine Uptake ◽  
Uptake Rates ◽  
Message Frames ◽  
Tweet Classification

BACKGROUND Exposure to anti-vaccine content on social media has been associated with delays and refusals of vaccinations, while pro-vaccine campaigns devised to disseminate the preventive benefits of vaccines have not succeeded in increasing vaccine uptake rates. Reasons remain unknown why anti-vaccine messaging hampers uptake while pro-vaccine campaigns do not improve it. OBJECTIVE We aim to identify reasons for the disparate effectiveness of anti- versus pro-vaccine social media content on vaccine delivery rates. In so doing, we apply the perspectives of message framing used in interpersonal health communication to explain why individuals refuse to adopt preventive behaviors. Specifically, we compare (1) the diversity, coherence, and distinctiveness of topics discussed by pro- and anti-vaccine communities and (2) message frames used to portray vaccines as a public health accomplishment or harmful agents. METHODS We developed a multimethod that combines the collection of a large amount of data from Twitter (~40,000 tweets), an automatic tweet classification algorithm, the K-means clustering algorithm, and a qualitative content analysis. RESULTS Our results show a larger number of topics (20 versus 17 clusters), greater coherence of topics (0.99 vs. 0.97) and distinctiveness of topics (1.22 vs. 1.31) among anti-vaccinists in comparison to pro-vaccinists. In addition, while anti-vaccinists use all four message frames known to make narratives persuasive and influential, pro-vaccinists neglect the problem statement. CONCLUSIONS Based on our results, we attribute the diversity, coherence, and distinctiveness of topics discussed among anti-vaccinists to their higher engagement, and we ascribe the influence of vaccine debate on uptake rates to the comprehensiveness of the message frames. These results show the urgency of developing value propositions for vaccines to counteract the negative impact of anti-vaccine content on the uptake rates. CLINICALTRIAL This study was determined to be a non-human subject study by Michigan State University’s Institutional Review Board (#STUDY00004514).

A Semantic-Based Algorithm for Microblogs Clustering

2013 ◽  
Vol 278-280 ◽  
pp. 1174-1177 ◽  
Author(s):  
Jia Jia Miao ◽  
Guo You Chen ◽  
Le Wang ◽  
Xue Lin Fang
Keyword(s):  
Mutual Information ◽  
Clustering Algorithm ◽  
Frequent Itemsets ◽  
Experimental Results ◽  
High Dimensional ◽  
Large Capacity ◽  
Current Affairs ◽  
Context Knowledge ◽  
Share Information

Microblogging has become a major tool for people to not only share information, but also to talk about current affairs. Has become the most popular content in the analysis, interested companies and researchers. We focus on the micro-blog clustering high-dimensional, high sparse, and proposed a new algorithm based on k-means-k frequent itemsets. In addition, the development of a method to capture long-term mutual information context knowledge in microblogging and algorithms are also designed to measure the conversation Similar. In order to support the new micro-blog clustering algorithm. Experimental results show that the clustering algorithm has higher accuracy than the standard k-means and two points in k-means algorithm toward large-capacity and highly sparse microblogging also maintain good scalability.

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