Incremental Slow Feature Analysis with Indefinite Kernel for Online Temporal Video Segmentation

IEEE Trans. on Image Process.

Pantić

2015

Self Cite

Abstract-Slow feature analysis (SFA) is a dimensionality reduction technique which has been linked to how visual brain cells work. In recent years, SFA was adopted for computer vision tasks. In this paper, we propose an exact kernel SFA (KSFA) framework for positive definite and indefinite kernels in Krein space. We then formulate an online KSFA which employs a reduced set expansion. Finally, by utilizing a special kind of kernel family, we formulate exact online KSFA for which no reduced set is required. We apply the proposed system to develop a SFA-based change detection algorithm for stream data. This framework is employed for temporal video segmentation and tracking. We test our setup on synthetic and real data streams. When combined with an online learning tracking system, the proposed change detection approach improves upon tracking setups that do not utilize change detection.

Section: B Contributionsmentioning

confidence: 99%

Section: B Contributionsmentioning

confidence: 99%

Online Kernel Slow Feature Analysis for Temporal Video Segmentation and Tracking

Liwicki

IEEE Trans. on Image Process.

Pantić

2015

Self Cite

“…However, until today limited research has been conducted regarding its efficacy on computer vision problems [8,13,14,15,26]. Recently, SFA and its discriminant extensions have been successfully applied for human action recognition in [26], while hierarchical segmentation of video sequences using SFA was investigated in [15].…”

Section: Introductionmentioning

confidence: 99%

“…In [8] SFA was applied for object and object-pose recognition on a homogeneous background, while in [14] SFA for vectorvalued functions was studied for blind source separation. Finally, an incremental SFA algorithm for change detection was proposed in [13].…”

Section: Introductionmentioning

confidence: 99%

Learning Slow Features for Behaviour Analysis

Nicolaou

2013 IEEE International Conference on Computer Vision

et al. 2013

Self Cite

A recently introduced latent feature learning technique for time varying dynamic phenomena analysis is the socalled Slow Feature Analysis (SFA). SFA is a deterministic component analysis technique for multi-dimensional sequences that by minimizing the variance of the first order time derivative approximation of the input signal finds uncorrelated projections that extract slowly-varying features ordered by their temporal consistency and constancy. In this paper, we propose a number of extensions in both the deterministic and the probabilistic SFA optimization frameworks. In particular, we derive a novel deterministic SFA algorithm that is able to identify linear projections that extract the common slowest varying features of two or more sequences. In addition, we propose an Expectation Maximization (EM) algorithm to perform inference in a probabilistic formulation of SFA and similarly extend it in order to handle two and more time varying data sequences. Moreover, we demonstrate that the probabilistic SFA (EM-SFA) algorithm that discovers the common slowest varying latent space of multiple sequences can be combined with dynamic time warping techniques for robust sequence timealignment. The proposed SFA algorithms were applied for facial behavior analysis demonstrating their usefulness and appropriateness for this task.

“…In [4], SFA was applied for object and object-pose recognition on a homogeneous background, while in [6] SFA for vector-valued functions was studied for blind source separation. Finally, an incremental SFA algorithm for change detection was proposed in [5].…”

mentioning

confidence: 99%

Probabilistic Slow Features for Behavior Analysis

Nicolaou

IEEE Trans. Neural Netw. Learning Syst.

et al. 2016

Self Cite

Abstract-A recently introduced latent feature learning technique for time-varying dynamic phenomena analysis is the so-called slow feature analysis (SFA). SFA is a deterministic component analysis technique for multidimensional sequences that, by minimizing the variance of the first-order time derivative approximation of the latent variables, finds uncorrelated projections that extract slowly varying features ordered by their temporal consistency and constancy. In this paper, we propose a number of extensions in both the deterministic and the probabilistic SFA optimization frameworks. In particular, we derive a novel deterministic SFA algorithm that is able to identify linear projections that extract the common slowest varying features of two or more sequences. In addition, we propose an expectation maximization (EM) algorithm to perform inference in a probabilistic formulation of SFA and similarly extend it in order to handle two and more time-varying data sequences. Moreover, we demonstrate that the probabilistic SFA (EM-SFA) algorithm that discovers the common slowest varying latent space of multiple sequences can be combined with dynamic time warping techniques for robust sequence time-alignment. The proposed SFA algorithms were applied for facial behavior analysis, demonstrating their usefulness and appropriateness for this task. IndexTerms-Behavior analysis, linear dynamical system (LDS), slow feature analysis (SFA), temporal alignment.