scholarly journals Video Data Retrieval using Image Color Histogram Technique

2019 ◽  
Vol 8 (6S3) ◽  
pp. 2056-2060 ◽  
Keyword(s):  
Semantic Analysis ◽  
Data Retrieval ◽  
Content Management ◽  
Video Data ◽  
Class Label ◽  
Knowledge Based ◽  
Current Content ◽  
Retrieval Result ◽  
Video Semantic Analysis ◽  
Indexing Mechanism

In this paper, a subspace-based multimedia datamining framework is proposed for video semantic analysis; specifically Current content management systems support retrieval using low-level features, such as motion, color, and texture. The proposed frameworks achieves full automation via a knowledge-based video indexing and retrieve an appropriate result, and replace a presented object with the retrieval result in real time. Along with this indexing mechanism a histogrambased color descriptors also introduced to reliably capture and represent the color properties of multiple images. Including of this a classification approach is also carried out by the classified associations and by assigning, each of them with a class label, and uses their appearances in the video to construct video indices. Our experimental results demonstrate the performance of the proposed approach.

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.

Content-Based Video Semantic Analysis

2011 ◽  
pp. 211-235 ◽  
Author(s):  
Shuqiang Jiang ◽  
Yonghong Tian ◽  
Qingming Huang ◽  
Tiejun Huang ◽  
Wen Gao
Keyword(s):  
Visual Attention ◽  
Object Detection ◽  
Video Analysis ◽  
Event Detection ◽  
Semantic Analysis ◽  
Research Area ◽  
Video Data ◽  
Computing Power ◽  
Detection And Tracking ◽  
Video Semantic Analysis

With the explosive growth in the amount of video data and rapid advance in computing power, extensive research efforts have been devoted to content-based video analysis. In this chapter, the authors will give a broad discussion on this research area by covering different topics such as video structure analysis, object detection and tracking, event detection, visual attention analysis, and so forth. In the meantime, different video representation and indexing models are also presented.

scholarly journals Sparsity Based Locality-Sensitive Discriminative Dictionary Learning for Video Semantic Analysis

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Ben-Bright Benuwa ◽  
Yongzhao Zhan ◽  
Benjamin Ghansah ◽  
Ernest K. Ansah ◽  
Andriana Sarkodie
Keyword(s):  
Dictionary Learning ◽  
Semantic Analysis ◽  
Learning Algorithm ◽  
Recognition Rate ◽  
Image Data ◽  
Classification Performance ◽  
Video Data ◽  
Video Feature ◽  
Video Semantic Analysis ◽  
Discriminative Dictionary Learning

Dictionary learning (DL) and sparse representation (SR) based classifiers have greatly impacted the classification performance and have had good recognition rate on image data. In video semantic analysis (VSA), the local structure of video data contains more vital discriminative information needed for classification. However, this has not been fully exploited by the current DL based approaches. Besides, similar coding findings are not being realized from video features with the same video category. Based on the issues stated afore, a novel learning algorithm, called 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 experiment results show that the proposed SLSDDL significantly improves the performance of video semantic detection compared with the comparative state-of-the-art approaches. Moreover, the robustness to various diverse environments in video is also demonstrated, which proves the universality of the novel approach.

scholarly journals Sparsity Based Locality –Sensitive Discriminative Dictionary Learning for Video Semantic Analysis

2018 ◽  
Author(s):  
Ben-Bright Benuwa ◽  
Yongzhao Zhan ◽  
Benjamin Ghansah ◽  
Ernest Ansah ◽  
Andriana Sarkodie
Keyword(s):  
Dictionary Learning ◽  
Semantic Analysis ◽  
Learning Algorithm ◽  
Recognition Rate ◽  
Image Data ◽  
Classification Performance ◽  
Video Data ◽  
Video Feature ◽  
Video Semantic Analysis ◽  
Discriminative Dictionary Learning

Dictionary Learning (DL) and Sparse Representation (SR) based Classifier have impacted greatly on the classification performance and has had good recognition rate on image data. In Video Semantic Analysis (VSA), the local structure of video data contains more vital discriminative information needed for classification. However, this has not been fully exploited by the current DL based approaches. Besides, similar coding findings are not being realized from video features with the same video category. Based on the issues stated afore, a novel learning algorithm, called 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 experiment results show that, the proposed SLSDDL significantly improves the performance of video semantic detection compared with the comparative state-of-the-art approaches. Moreover, the robustness to various diverse environments in video is also demonstrated, which proves the universality of the novel approach.

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

scholarly journals SIAT: A Distributed Video Analytics Framework for Intelligent Video Surveillance

Symmetry ◽  
2019 ◽  
Vol 11 (7) ◽  
pp. 911 ◽  
Author(s):  
Md Azher Uddin ◽  
Aftab Alam ◽  
Nguyen Anh Tu ◽  
Md Siyamul Islam ◽  
Young-Koo Lee
Keyword(s):  
Distributed Computing ◽  
Real Time ◽  
Video Surveillance ◽  
Video Processing ◽  
Large Scale ◽  
Distributed Processing ◽  
Content Management ◽  
Video Data ◽  
Video Analytics ◽  
Intelligent Video Surveillance

In recent years, the amount of intelligent CCTV cameras installed in public places for surveillance has increased enormously and as a result, a large amount of video data is produced every moment. Due to this situation, there is an increasing request for the distributed processing of large-scale video data. In an intelligent video analytics platform, a submitted unstructured video undergoes through several multidisciplinary algorithms with the aim of extracting insights and making them searchable and understandable for both human and machine. Video analytics have applications ranging from surveillance to video content management. In this context, various industrial and scholarly solutions exist. However, most of the existing solutions rely on a traditional client/server framework to perform face and object recognition while lacking the support for more complex application scenarios. Furthermore, these frameworks are rarely handled in a scalable manner using distributed computing. Besides, existing works do not provide any support for low-level distributed video processing APIs (Application Programming Interfaces). They also failed to address a complete service-oriented ecosystem to meet the growing demands of consumers, researchers and developers. In order to overcome these issues, in this paper, we propose a distributed video analytics framework for intelligent video surveillance known as SIAT. The proposed framework is able to process both the real-time video streams and batch video analytics. Each real-time stream also corresponds to batch processing data. Hence, this work correlates with the symmetry concept. Furthermore, we introduce a distributed video processing library on top of Spark. SIAT exploits state-of-the-art distributed computing technologies with the aim to ensure scalability, effectiveness and fault-tolerance. Lastly, we implant and evaluate our proposed framework with the goal to authenticate our claims.

DWT-BASED VIDEO DATA HIDING ROBUST TO MPEG COMPRESSION AND FRAME LOSS

2005 ◽  
Vol 05 (01) ◽  
pp. 111-133 ◽  
Author(s):  
HONGMEI LIU ◽  
JIWU HUANG ◽  
YUN Q. SHI
Keyword(s):  
Data Hiding ◽  
Random Noise ◽  
Data Retrieval ◽  
Video Data ◽  
Experimental Results ◽  
Data Detection ◽  
Discrete Wavelet ◽  
Magnitude Distribution ◽  
Embedding Strategy ◽  
Error Probabilities

In this paper, we propose a blind video data-hiding algorithm in DWT (discrete wavelet transform) domain. It embeds multiple information bits into uncompressed video sequences. The major features of this algorithm are as follows. (1) Development of a novel embedding strategy in DWT domain. Different from the existing schemes based on DWT that have explicitly excluded the LL subband coefficients from data embedding, we embed data in the LL subband for better invisibility and robustness. The underlying idea comes from our qualitative and quantitative analysis of the DWT coefficients magnitude distribution over commonly used images. The experimental results confirm the superiority of the proposed embedding strategy. (2) To combat temporal attacks, which will destroy the synchronization of hidden data that is necessary in data retrieval, we develop an effective temporal synchronization technique. Compared with the sliding correlation proposed in the existing algorithms, our synchronization technique is more advanced. (3) We adopt a new 3D interleaving technique to combat bursts of errors, while reducing random error probabilities in data detection by exploiting ECC (error correcting coding). The detection error rate with 3D interleaving is much lower than that without 3D interleaving when frame loss rate is below 50%. (4) Take a carefully designed measure in bit embedding to guarantee the invisibility of information. In experiments, we can embed a string of 402 bytes (excluding the redundant bits associated with ECC) in 96 frames of the CIF format sequence. The experimental results have demonstrated that the embedded information bits are perceptually transparent when the frames in the sequence are viewed either as still images or played continuously. The hidden information is robust to manipulations, such as MPEG-2 compression, scaling, additive random noise, and frame loss.

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
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