Automatic generation of soccer video content hierarchy by mapping low-level features to high-level semantics

2003 ◽  
Author(s):  
Jianyun Chen ◽  
Yunhao Li ◽  
Song-Yang Lao ◽  
Ling-Da Wu
2001 ◽  
Vol 01 (01) ◽  
pp. 63-81 ◽  
Author(s):  
ALAN HANJALIC ◽  
REGINALD L. LAGENDIJK ◽  
JAN BIEMOND

This paper addresses the problem of automatically partitioning a video into semantic segments using visual low-level features only. Semantic segments may be understood as building content blocks of a video with a clear sequential content structure. Examples are reports in a news program, episodes in a movie, scenes of a situation comedy or topic segments of a documentary. In some video genres like news programs or documentaries, the usage of different media (visual, audio, speech, text) may be beneficial or is even unavoidable for reliably detecting the boundaries between semantic segments. In many other genres, however, the pay-off in using different media for the purpose of high-level segmentation is not high. On the one hand, relating the audio, speech or text to the semantic temporal structure of video content is generally very difficult. This is especially so in "acting" video genres like movies and situation comedies. On the other hand, the information contained in the visual stream of these video genres often seems to provide the major clue about the position of semantic segments boundaries. Partitioning a video into semantic segments can be performed by measuring the coherence of the content along neighboring video shots of a sequence. The segment boundaries are then found at places (e.g., shot boundaries) where the values of content coherence are sufficiently low. On the basis of two state-of-the-art techniques for content coherence modeling, we illustrate in this paper the current possibilities for detecting the boundaries of semantic segments using visual low-level features only.


Author(s):  
Nathan Saraiva ◽  
Nazrul Islam ◽  
Danny Alex Lachos Perez ◽  
Christian Esteve Rothenberg

Year after year, the growth of video traffic over the Internet keeps increasing. Video streaming over best-effort networks is considered inefficient and inappropriate to meet the expected Quality of Experience (QoE) of the new generation of multimedia services. Over the past few years, a number of technologies have emerged to improve the state of the art of video delivery, including HTTP Adaptive Streaming (HAS) that adapts the bitrate according to network conditions. At the crossroads, Software Defined Networking (SDN) offers options to meet Quality of Service (QoS) objectives for improved video quality by exploiting end-to-end programmability of network behaviour. However, traditional SDN approaches require dealing with low-level details from the underlying infrastructure, interfering in the automation and agility of service deployments. To alleviate these issues and overall provide a simpler approach, Intent-Based Networking (IBN) is being proposed to abstract low-level configurations through high-level policy interfaces. In this paper, we explore such an approach by implementing intent-based control loops for video service assurance. The proposed methods dynamically reconfigure the network for service-specific requirements using IBN to define the high-level behavior. We experimentally evaluate a use case where video traffic is rerouted based on network conditions to improve the QoS. The Proof-of-Concept results point to the potential of improving video content delivery through QoS-aware Intent-based approaches.


2015 ◽  
Vol 2015 ◽  
pp. 1-10
Author(s):  
Shao-nian Huang ◽  
Dong-jun Huang ◽  
Mansoor Ahmed Khuhro

Video event detection is a challenging problem in many applications, such as video surveillance and video content analysis. In this paper, we propose a new framework to perceive high-level codewords by analyzing temporal relationship between different channels of video features. The low-level vocabulary words are firstly generated after different audio and visual feature extraction. A weighted undirected graph is constructed by exploring the Granger Causality between low-level words. Then, a greedy agglomerative graph-partitioning method is used to discover low-level word groups which have similar temporal pattern. The high-level codebooks representation is obtained by quantification of low-level words groups. Finally, multiple kernel learning, combined with our high-level codewords, is used to detect the video event. Extensive experimental results show that the proposed method achieves preferable results in video event detection.


2019 ◽  
Vol 1 (1) ◽  
pp. 31-39
Author(s):  
Ilham Safitra Damanik ◽  
Sundari Retno Andani ◽  
Dedi Sehendro

Milk is an important intake to meet nutritional needs. Both consumed by children, and adults. Indonesia has many producers of fresh milk, but it is not sufficient for national milk needs. Data mining is a science in the field of computers that is widely used in research. one of the data mining techniques is Clustering. Clustering is a method by grouping data. The Clustering method will be more optimal if you use a lot of data. Data to be used are provincial data in Indonesia from 2000 to 2017 obtained from the Central Statistics Agency. The results of this study are in Clusters based on 2 milk-producing groups, namely high-dairy producers and low-milk producing regions. From 27 data on fresh milk production in Indonesia, two high-level provinces can be obtained, namely: West Java and East Java. And 25 others were added in 7 provinces which did not follow the calculation of the K-Means Clustering Algorithm, including in the low level cluster.


Author(s):  
Margarita Khomyakova

The author analyzes definitions of the concepts of determinants of crime given by various scientists and offers her definition. In this study, determinants of crime are understood as a set of its causes, the circumstances that contribute committing them, as well as the dynamics of crime. It is noted that the Russian legislator in Article 244 of the Criminal Code defines the object of this criminal assault as public morality. Despite the use of evaluative concepts both in the disposition of this norm and in determining the specific object of a given crime, the position of criminologists is unequivocal: crimes of this kind are immoral and are in irreconcilable conflict with generally accepted moral and legal norms. In the paper, some views are considered with regard to making value judgments which could hardly apply to legal norms. According to the author, the reasons for abuse of the bodies of the dead include economic problems of the subject of a crime, a low level of culture and legal awareness; this list is not exhaustive. The main circumstances that contribute committing abuse of the bodies of the dead and their burial places are the following: low income and unemployment, low level of criminological prevention, poor maintenance and protection of medical institutions and cemeteries due to underperformance of state and municipal bodies. The list of circumstances is also open-ended. Due to some factors, including a high level of latency, it is not possible to reflect the dynamics of such crimes objectively. At the same time, identification of the determinants of abuse of the bodies of the dead will reduce the number of such crimes.


2021 ◽  
pp. 002224372199837
Author(s):  
Walter Herzog ◽  
Johannes D. Hattula ◽  
Darren W. Dahl

This research explores how marketing managers can avoid the so-called false consensus effect—the egocentric tendency to project personal preferences onto consumers. Two pilot studies were conducted to provide evidence for the managerial importance of this research question and to explore how marketing managers attempt to avoid false consensus effects in practice. The results suggest that the debiasing tactic most frequently used by marketers is to suppress their personal preferences when predicting consumer preferences. Four subsequent studies show that, ironically, this debiasing tactic can backfire and increase managers’ susceptibility to the false consensus effect. Specifically, the results suggest that these backfire effects are most likely to occur for managers with a low level of preference certainty. In contrast, the results imply that preference suppression does not backfire but instead decreases false consensus effects for managers with a high level of preference certainty. Finally, the studies explore the mechanism behind these results and show how managers can ultimately avoid false consensus effects—regardless of their level of preference certainty and without risking backfire effects.


Author(s):  
Richard Stone ◽  
Minglu Wang ◽  
Thomas Schnieders ◽  
Esraa Abdelall

Human-robotic interaction system are increasingly becoming integrated into industrial, commercial and emergency service agencies. It is critical that human operators understand and trust automation when these systems support and even make important decisions. The following study focused on human-in-loop telerobotic system performing a reconnaissance operation. Twenty-four subjects were divided into groups based on level of automation (Low-Level Automation (LLA), and High-Level Automation (HLA)). Results indicated a significant difference between low and high word level of control in hit rate when permanent error occurred. In the LLA group, the type of error had a significant effect on the hit rate. In general, the high level of automation was better than the low level of automation, especially if it was more reliable, suggesting that subjects in the HLA group could rely on the automatic implementation to perform the task more effectively and more accurately.


2020 ◽  
Vol 4 (POPL) ◽  
pp. 1-32 ◽  
Author(s):  
Michael Sammler ◽  
Deepak Garg ◽  
Derek Dreyer ◽  
Tadeusz Litak
Keyword(s):  

2021 ◽  
pp. 0308518X2199781
Author(s):  
Xinyue Luo ◽  
Mingxing Chen

The nodes and links in urban networks are usually presented in a two-dimensional(2D) view. The co-occurrence of nodes and links can also be realized from a three-dimensional(3D) perspective to make the characteristics of urban network more intuitively revealed. Our result shows that the external connections of high-level cities are mainly affected by the level of cities(nodes) and less affected by geographical distance, while medium-level cities are affected by the interaction of the level of cities(nodes) and geographical distance. The external connections of low-level cities are greatly restricted by geographical distance.


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