From Video to Semantic: Video Semantic Analysis Technology Based on Knowledge Graph

2019 ◽  
Vol 09 (08) ◽  
pp. 1584-1590
Author(s):  
莉琼 邓
Keyword(s):  
Semantic Analysis ◽  
Knowledge Graph ◽  
Video Semantic Analysis

Video semantic analysis based kernel locality-sensitive discriminative sparse representation

2019 ◽  
Vol 119 ◽  
pp. 429-440 ◽  
Author(s):  
Ben-Bright Benuwa ◽  
Yongzhao Zhan ◽  
Augustine Monney ◽  
Benjamin Ghansah ◽  
Ernest K. Ansah
Keyword(s):  
Sparse Representation ◽  
Semantic Analysis ◽  
Video Semantic Analysis

Temporally Consistent Gaussian Random Field for Video Semantic Analysis

2007 ◽  
Author(s):  
Jinhui Tang ◽  
Xian-Sheng Hua ◽  
Tao Mei ◽  
Guo-Jun Qi ◽  
Shipeng Li ◽  
...  
Keyword(s):  
Random Field ◽  
Semantic Analysis ◽  
Gaussian Random Field ◽  
Video Semantic Analysis

BUILD KNOWLEDGE GRAPH FROM HETEROGENEOUS DOCUMENTS

2021 ◽  
Vol 47 (05) ◽  
Author(s):  
NGUYỄN CHÍ HIẾU
Keyword(s):  
Information Retrieval ◽  
Deep Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Question Answering ◽  
Semantic Analysis ◽  
Knowledge Graph ◽  
Question Answering Systems ◽  
Knowledge Graphs

Knowledge Graphs are applied in many fields such as search engines, semantic analysis, and question answering in recent years. However, there are many obstacles for building knowledge graphs as methodologies, data and tools. This paper introduces a novel methodology to build knowledge graph from heterogeneous documents.  We use the methodologies of Natural Language Processing and deep learning to build this graph. The knowledge graph can use in Question answering systems and Information retrieval especially in Computing domain

Video Semantic Analysis

2021 ◽  
Vol 11 (2) ◽  
pp. 1-21
Author(s):  
Daniel Danso Essel ◽  
Ben-Bright Benuwa ◽  
Benjamin Ghansah
Keyword(s):  
Dictionary Learning ◽  
Semantic Analysis ◽  
Learning Algorithm ◽  
Recognition Rate ◽  
Image Data ◽  
Video Data ◽  
The Novel ◽  
Video Feature ◽  
Novel Approach ◽  
Video Semantic Analysis

Sparse Representation (SR) and Dictionary Learning (DL) based Classifier have shown promising results in classification tasks, with impressive recognition rate on image data. In Video Semantic Analysis (VSA) however, the local structure of video data contains significant discriminative information required for classification. To the best of our knowledge, this has not been fully explored by recent DL-based approaches. Further, similar coding findings are not being realized from video features with the same video category. Based on the foregoing, a novel learning algorithm, Sparsity based Locality-Sensitive Discriminative Dictionary Learning (SLSDDL) for VSA is proposed in this paper. In the proposed algorithm, a discriminant loss function for the category based on sparse coding of the sparse coefficients is introduced into structure of Locality-Sensitive Dictionary Learning (LSDL) algorithm. Finally, the sparse coefficients for the testing video feature sample are solved by the optimized method of SLSDDL and the classification result for video semantic is obtained by minimizing the error between the original and reconstructed samples. The experimental results show that, the proposed SLSDDL significantly improves the performance of video semantic detection compared with state-of-the-art approaches. The proposed approach also shows robustness to diverse video environments, proving the universality of the novel approach.

A Framework Based on Multi-models and Multi-features for Sports Video Semantic Analysis

2012 ◽  
Vol 11 (10) ◽  
pp. 1381-1390 ◽  
Author(s):  
Jiaqi Fu ◽  
Hongping Hu ◽  
Richao Chen ◽  
Heng Ren
Keyword(s):  
Semantic Analysis ◽  
Sports Video ◽  
Video Semantic Analysis

scholarly journals Engineering Knowledge Graph for Keyword Discovery in Patent Search

2019 ◽  
Vol 1 (1) ◽  
pp. 2249-2258
Author(s):  
Serhad Sarica ◽  
Binyang Song ◽  
En Low ◽  
Jianxi Luo
Keyword(s):  
Language Processing ◽  
Semantic Analysis ◽  
Knowledge Graph ◽  
Discovery Process ◽  
Human Errors ◽  
Engineering Knowledge ◽  
Patent Retrieval ◽  
The Core ◽  
Patent Search ◽  
Human Effort

AbstractPatent retrieval and analytics have become common tasks in engineering design and innovation. Keyword-based search is the most common method and the core of integrative methods for patent retrieval. Often searchers intuitively choose keywords according to their knowledge on the search interest which may limit the coverage of the retrieval. Although one can identify additional keywords via reading patent texts from prior searches to refine the query terms heuristically, the process is tedious, time-consuming, and prone to human errors. In this paper, we propose a method to automate and augment the heuristic and iterative keyword discovery process. Specifically, we train a semantic engineering knowledge graph on the full patent database using natural language processing and semantic analysis, and use it as the basis to retrieve and rank the keywords contained in the retrieved patents. On this basis, searchers do not need to read patent texts but just select among the recommended keywords to expand their queries. The proposed method improves the completeness of the search keyword set and reduces the human effort for the same task.

Group sparse based locality – sensitive dictionary learning for video semantic analysis

2018 ◽  
Vol 78 (6) ◽  
pp. 6721-6744 ◽  
Author(s):  
Ben-Bright Benuwa ◽  
Yongzhao Zhan ◽  
JunQi Liu ◽  
Jianping Gou ◽  
Benjamin Ghansah ◽  
...  
Keyword(s):  
Dictionary Learning ◽  
Semantic Analysis ◽  
Video Semantic Analysis
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