8,431 Hits in 9.1 sec

Perceptual Texture Similarity for Machine Intelligence Applications [chapter]

Karam Naser, Vincent Ricordel, Patrick Le Callet
2017 Visual Content Indexing and Retrieval with Psycho-Visual Models  
The details of texture perception, covering both static texture and motion perception, is given in Sec. 3. The models of texture similarity is reviewed in Sec. 4, with benchmarking tools in Sec. 5.  ...  The application of texture similarity models in image and video compression is discussed in 6, and the conclusion is given in Sec. 7.  ...  Motion Based Modeling The motion based analysis and modeling of dynamic textures has been in large body of studies.  ... 
doi:10.1007/978-3-319-57687-9_2 fatcat:lnr5un6agbfqzcaqmuucmghecq

A Synthetic Video Dataset for Video Compression Evaluation

Di Ma, Angeliki V. Katsenou, David R. Bull
2019 2019 IEEE International Conference on Image Processing (ICIP)  
This will support research in video compression enabling researchers to understand and model the relationship between video content and its coding parameters.  ...  Full terms of use are available: ABSTRACT In this paper, a new Synthetic video Texture dataset (SynTex) is introduced.  ...  Based on previous studies on the analysis of video content for video compression purposes [4, 17] , video textures are classified into three types, static (e.g. a camera panning over a still scenery)  ... 
doi:10.1109/icip.2019.8803798 dblp:conf/icip/MaKB19 fatcat:f46gfwdgbjdqdnmef3ogdpteoe

Dynamic Texture Recognition Based on Compression Artifacts [chapter]

Dubravko Ćulibrk, Matei Mancas, Vladimir Ćrnojevic
2013 Studies in Fuzziness and Soft Computing  
Although one of the most important prospective applications of the technology is content-based video retrieval, recognition of dynamic textures for compressed video has not been considered.  ...  The content of video and dynamic textures in particular, profoundly affect the performance of video compression algorithms.  ...  Based on video quality measures, content-dependent features are extracted for the frames of the video.  ... 
doi:10.1007/978-3-642-30278-7_20 fatcat:heebdyqpmjcuznjswe7syvbnm4

Understanding video texture — A basis for video compression

Angeliki V. Katsenou, Thomas Ntasios, Mariana Afonso, Dimitris Agrafiotis, David R. Bull
2017 2017 IEEE 19th International Workshop on Multimedia Signal Processing (MMSP)  
Therefore, we have used a feature selection method based on Random Forest (RF) models [19] .  ...  Fig. 2 : 2 Feature ranking based on RF model that uses the Mean Decrease Gini Impurity metric.  ... 
doi:10.1109/mmsp.2017.8122252 dblp:conf/mmsp/KatsenouNAAB17 fatcat:qqjaywrntbaxndafvqzdwozhxu

Facial Expression Recognition from Video Sequences Based on Spatial-Temporal Motion Local Binary Pattern and Gabor Multiorientation Fusion Histogram

Lei Zhao, Zengcai Wang, Guoxin Zhang
2017 Mathematical Problems in Engineering  
Then, spatial-temporal motion local binary pattern (LBP) feature is extracted and integrated with Gabor multiorientation fusion histogram to give descriptors, which reflect static and dynamic texture information  ...  This paper proposes novel framework for facial expressions analysis using dynamic and static information in video sequences.  ...  Based on dynamic Bayesian network (DBN) for spontaneous AU intensity recognition, unified probabilistic model is proposed in [32] .  ... 
doi:10.1155/2017/7206041 fatcat:imyvx5morrf6jedthcr6rh3zje

Motion Textures: Modeling, Classification, and Segmentation Using Mixed-State Markov Random Fields

Tomás Crivelli, Bruno Cernuschi-Frias, Patrick Bouthemy, Jian-Feng Yao
2013 SIAM Journal of Imaging Sciences  
First, we present a method for recognition and classification of motion textures, by means of the Kullback-Leibler distance between mixed-state statistical models.  ...  The motivation of the proposed formulation runs toward the analysis of dynamic video contents, and we tackle two related problems.  ...  We have proposed a new approach to dynamic texture modeling, based on a spatial statistical parametric model of the apparent motion extracted from video sequences.  ... 
doi:10.1137/120872048 fatcat:hyuv4jwqxzdgzjsyq4okrpae4y

Recognition of Dynamic Video Contents With Global Probabilistic Models of Visual Motion

G. Piriou, P. Bouthemy, J.-F. Yao
2006 IEEE Transactions on Image Processing  
To validate the interest of the proposed motion modeling and recognition framework, we report dynamic content recognition results on sports videos.  ...  In this paper, we tackle the challenging problem of recognizing dynamic video contents from low-level motion features.  ...  ACKNOWLEDGMENT The authors would like to thank INA for providing the video sequences.  ... 
doi:10.1109/tip.2006.881963 pmid:17076401 fatcat:cclh322rt5fxze5qzfnnbi2vj4

Study of Compression Statistics and Prediction of Rate-Distortion Curves for Video Texture [article]

Angeliki V. Katsenou, Mariana Afonso, David R. Bull
2021 arXiv   pre-print
In this paper, we analyse the spatio-temporal features and statistics of video textures, explore the rate-quality performance of different texture types and investigate models to mathematically describe  ...  All experiments were performed on homogeneous video textures to ensure validity of the observations.  ...  Analysing Video Content for Compression Purposes The literature is rich in contributions relating to texture analysis (22; 23; 24) but most of these are in the context of computer vision-based recognition  ... 
arXiv:2102.04167v1 fatcat:5ksp6exsgfgkrag6fgwwnp6qaq

Description of Rotation-Invariant Textures using Local Binary Pattern Features

Prashant H.Gutte, Prashant K. Kharat
2014 International Journal of Computer Applications  
Uniform Local binary pattern (LBP) is a combination of structural and statistical analysis model for classification of both static and dynamic textures.  ...  Texture classification is one of the most interesting research topics in the field of computer vision. This paper aims at classifying static as well as dynamic textures (DT).  ...  For static textures, features which are independent of angle to input texture image is known as rotation-invariant features. Statistical and Model based methods are two broad ways for classification.  ... 
doi:10.5120/17404-7969 fatcat:dvqblydxmjfavhdjulxbnwpv6a


2017 International Journal of Advance Engineering and Research Development  
The volume of the video content grows very fast and most of the video search systems are based on manual annotations or use text information.  ...  Before retrieving the video based on its content, some pre-processing has to be performed on video like video segmentation, key frame extraction and feature extraction.  ...  This descriptor is should be used for video re-purposing, surveillance, fast browsing, dynamic video summarization, content-based querying, etc.  ... 
doi:10.21090/ijaerd.99438 fatcat:vbmr7awdf5ao5n677bwggbcq2a

Nonparametric motion characterization using causal probabilistic models for video indexing and retrieval

R. Fablet, P. Bouthemy, P. Perez
2002 IEEE Transactions on Image Processing  
We aim at providing a global interpretation of the dynamic content of video shots without any prior motion segmentation and without any use of dense optic flow fields.  ...  This paper describes an original approach for content-based video indexing and retrieval.  ...  corpus and to MIT for supplying the sequences of temporal textures fire and river.  ... 
doi:10.1109/tip.2002.999674 pmid:18244642 fatcat:vaiewjrk5nesxeck27yrimd4ca

Two-Stream Convolutional Networks for Dynamic Texture Synthesis

Matthew Tesfaldet, Marcus A. Brubaker, Konstantinos G. Derpanis
2018 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition  
Given an input dynamic texture, statistics of filter responses from the object recognition ConvNet encapsulate the per-frame appearance of the input texture, while statistics of filter responses from the  ...  Our model is based on pre-trained convolutional networks (ConvNets) that target two independent tasks: (i) object recognition, and (ii) optical flow prediction.  ...  This research was undertaken as part of the Vision: Science to Applications program, thanks in part to funding from the Canada First Research Excellence Fund.  ... 
doi:10.1109/cvpr.2018.00701 dblp:conf/cvpr/TesfaldetBD18 fatcat:xltjab4otvhpzf3hxf7fnxsozq

Gait recognition based on improved dynamic Bayesian networks

Changhong Chen, Jimin Liang, Xiuchang Zhu
2011 Pattern Recognition  
, the hidden Markov model (HMM) and the dynamic texture (DT) model to gait recognition.  ...  We demonstrated the validity of LTSM with experiments on both the CMU Mobo gait database and CASIA gait database (dataset B), and that of LDT on the CMU Mobo gait database.  ...  Therefore, these methods are only validated based on activity recognition of a small database or classifying gait styles.  ... 
doi:10.1016/j.patcog.2010.10.021 fatcat:jx2vpxqe4bgsjajn6op73eauym

Syntactical and Semantical Description of Video Sequences [chapter]

N. Luth, A. Miene, P. Alshuth
1999 Database Semantics  
We describe a complete system for encoding the contents of video sequences based on their syntactical and semantical description. Our techniques are built on two phases: analysis and synthesis.  ...  The latter is an useful approach for automatic trailer generation and for intelligent video editing as add-on for a video-cut-system.  ...  The result of our texture analysis is an automatically generated texture description based on visual properties of textures.  ... 
doi:10.1007/978-0-387-35561-0_6 fatcat:lisfkwes6rfspbgxume3rjlzgu

Dynamic texture and scene classification by transferring deep image features [article]

Xianbiao Qi, Chun-Guang Li, Guoying Zhao, Xiaopeng Hong, Matti Pietikäinen
2015 arXiv   pre-print
Dynamic texture and scene classification are two fundamental problems in understanding natural video content.  ...  Inspired by the success of deep structures in image classification, we attempt to leverage a deep structure to extract feature for dynamic texture and scene classification.  ...  LDS is a statistical generative model which captures the spatial appearance and dynamics in a video [14] .  ... 
arXiv:1502.00303v1 fatcat:pbg6n74ysjd5phwzrivmt7siea
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