Content analysis of video using principal components

Sahouria, Emile; Zakhor, Avideh

doi:10.1109/icip.1998.727323

Cited by 25 publications

(33 citation statements)

References 21 publications

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“…Firstly LSA was presented as a text analysis method when the features are represented by terms occurring in the considered text [2]. Subsequently LDA has been employed on image analysis [15], video data [16] and music or audio analysis [17]. The main objective of the LSA process is to produce a mapping into a "latent semantic space" also called Latent Topic Space.…”

Section: Latent Semantic Analysismentioning

confidence: 99%

Variable Selection and Feature Extraction Through Artificial Intelligence Techniques

Cateni¹,

Vannucci²,

Vannocci³

et al. 2013

Multivariate Analysis in Management, Engineering and the Sciences

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Section: Latent Semantic Analysismentioning

confidence: 99%

Variable Selection and Feature Extraction Through Artificial Intelligence Techniques

Cateni¹,

Vannucci²,

Vannocci³

et al. 2013

Multivariate Analysis in Management, Engineering and the Sciences

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“…To reduce the complexity of building a generative model of motion, Principal Component Analysis (PCA) [15] is used for dimensionality reduction.…”

Section: Eigenspace Representationmentioning

confidence: 99%

Real-time motion control using pose space probability density estimation

Okwechime

Ong

Bowden

2009

2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops

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The ability to control the movements of an object or person in a video sequence has applications in the movie and animation industries, and in HCI. In this paper, we introduce a new algorithm for real-time motion control and demonstrate its application to pre-recorded video clips and HCI. Firstly, a dataset of video frames are projected into a lower dimension space. A k-mediod clustering algorithm with a distance metric is used to determine groups of similar frames which operate as cut points, segmenting the data into smaller subsequences. A multivariate probability distribution is learnt and probability density estimation is used to determine transitions between the subsequences to develop novel motion. To facilitate real-time control, conditional probabilities are used to derive motion given user commands. The motion controller is extended to HCI using speech Mel-Frequency Ceptral Coefficients (MFCCs) to trigger movement from an input speech signal. We demonstrate the flexibility of the model by presenting results ranging from datasets composed of both vectorised images and 2D point representation. Results show plausible motion generation and lifelike blends between different types of movement.

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“…Meanwhile, subspace data analysis methods have been used widely in image processing [11] [4], and recently in the area of content-based video indexing and retrieval [10]. Sahouria and Zakhor [10] used PCA to reduce the dimensionality of features (colour histograms, motion vectors) of video frames for the purpose of detecting scene changes and characterising an entire sport video by motion activities, respectively.…”

Section: Introductionmentioning

confidence: 99%

“…Sahouria and Zakhor [10] used PCA to reduce the dimensionality of features (colour histograms, motion vectors) of video frames for the purpose of detecting scene changes and characterising an entire sport video by motion activities, respectively.…”

Section: Introductionmentioning

confidence: 99%

Video classification using spatial-temporal features and PCA

2003

2003 International Conference on Multimedia and Expo. ICME '03. Proceedings (Cat. No.03TH8698)

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We investigate the problem of automated video classification by analysing the low-level audio-visual signal patterns along the time course in a holistic manner. Five popular TV broadcast genre are studied including sports, cartoon, news, commercial and music. A novel statistically based approach is proposed comprising two important ingredients designed for implicit semantic content characterisation and class identities modelling. First, a spatial-temporal audio-visual "super" feature vector is computed, capturing crucial clip-level video structure information inherent in a video genre. Second, the feature vector is further processed using Principal Component Analysis to reduce the spatial-temporal redundancy while exploiting the correlations between feature elements, which give rise to a compact representation for effective probabilistic modelling of each video genre. Extensive experiments are conducted assessing various aspects of the approach and their influence on the overall system performance.

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Content analysis of video using principal components

Cited by 25 publications

References 21 publications

Variable Selection and Feature Extraction Through Artificial Intelligence Techniques

Variable Selection and Feature Extraction Through Artificial Intelligence Techniques

Real-time motion control using pose space probability density estimation

Video classification using spatial-temporal features and PCA

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