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Spatio-Temporal Sequence Learning of Visual Place Cells for Robotic Navigation. IJCNN, WCCI, Barcelona, Spain, 2010. Nguyen Vu Anh, Alex Leng-Phuan Tay, Wooi-Boon Goh School of Computer Engineering Nanyang Technological University Singapore. Janusz A. Starzyk

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spatio temporal sequence learning of visual place cells for robotic navigation
Spatio-Temporal Sequence Learning of Visual Place Cells for Robotic Navigation

IJCNN, WCCI, Barcelona, Spain, 2010

Nguyen Vu Anh, Alex Leng-Phuan Tay,

Wooi-Boon Goh

School of Computer Engineering

Nanyang Technological University

Singapore

Janusz A. Starzyk

School of Electrical Engineering

Ohio University

Athens, USA

presented by Nguyen Vu Anh

date: 20th July, 2010

outline
Outline
  • Introduction
  • HMAX Feature Building and Extraction
  • Spatio-Temporal Learning and Recognition
  • Empirical Results
  • Conclusion and future directions
introduction
Introduction
  • Robotic navigation: Localization and Mapping.
    • Topological map & Place cells
    • Scope: Topological Visual Localization
  • Challenges:
    • High dimension and uncertainty of visual features
    • Perceptual aliasing
    • Complex probabilistic frameworks e.g. HMM
  • Approach:
    • Structural organization of human memory architecture.
    • Short-Term Memory (STM) and Long-Term Memory(LTM) Interaction
introduction1
Introduction

Classifier

  • System Architecture

SequenceStorage

Symbol Quantization

Feature Building and Extraction

introduction2
Introduction
  • Existing Works:
    • Autonomous navigation (SLAM): Mapping, Localization and Path Planning
      • Topological vs metric representation
      • Human employs mainly topological representation of environment[O’Keefe (1976), Redish(1999), Eichenbaum (1999), etc]
    • Visual Place-cell model: [Torralba (2001) ; Renninger&Malik (2004) ; Siagian&Itti (2007)]
      • Hierarchical feature building and extraction (HMAX Model) [Serre et al (2007)]
    • Spatio-Temporal sequence learning: [Wang&Arbib (1990) (1993), Wang&Yowono (1995)]
      • Our previous works: [Starzyk&He, (2007);Starzyk&He (2009);Tay et al (2007);Nguyen&Tay (2009)]
hmax feature building and extraction
HMAX Feature Building and Extraction
  • Interleaving simple (S) and complex (C) layers with increasing spatial invariance (Retina - LGN – V1 – V2,V4)
  • 2 Stages:
    • Feature Construction
    • Feature Extraction
  • Feature Significance:
hmax feature building and extraction1
HMAX Feature Building and Extraction

Dot-Product Matching

Spatial Invariance Processing

Prototypes

Ref: Riesenhuber & Poggio (1999),Serre et al (2007)

spatio temporal learning architecture
Spatio-Temporal Learning Architecture
  • STM Structure:
    • Quantization of input using KFLANN with vigilance ρ

See: Tay, Zurada,Wong and Xu, TNN, 2007

spatio temporal learning architecture1
Spatio-Temporal Learning Architecture
  • STM Structure:

See: Tay, Zurada,Wong and Xu, TNN, 2007

spatio temporal learning architecture2
Spatio-Temporal Learning Architecture
  • LTM Cell Structure:
    • Each LTM is learnt by one-shot mechanism.
    • Each long training sequence is segmented into N overlapping subsequences of the same length M.
    • Each subsequence is dedicated permanently to an LTM cell.
spatio temporal learning architecture3
Spatio-Temporal Learning Architecture
  • LTM Cell Structure:

Dual Neurons – STM

Primary Neurons – Primary Excitation

spatio temporal learning architecture4
Spatio-Temporal Learning Architecture
  • Storage
    • One-shot learning
  • Recognition

Input feature vector

Primary ExcitationComputation

Dual Neurons Update – Evidence Accumulation

Output Matching Score from the last DN

empirical results
Empirical Results
  • ICLEF Competition 2010 Dataset
    • 9 classes of places
    • 2 sets of images with the same trajectory (Set S and SetC) (~4000 images each set)

C

K

L

O

empirical results1
Empirical Results
  • Task
    • 1 sequence (Set S) as training set and 1 sequence as testing set (Set R).
  • Features:
    • 10% of the training sequence
  • Training
    • ρ=0.7.
    • Segmentation into consecutive subsequences of equal length (100) with overlapping portion (>50%).
    • Each subsequence is stored as a LTM cell.
    • The label of each LTM cell is the majority label of individual components.
  • Testing
    • The label is assigned as the label of the maximally activated LTM cell.
    • If the activation of the maximal activated LTM cell is below ө, the system refuses to assign the label.
empirical results2
Empirical Results

Table: LTM listing with training set S

empirical results3
Empirical Results
  • Accuracy without threshold
  • Accuracy with threshold ө=0.4
  • Robust testing: missing elements
empirical results4
Empirical Results

Figure: LTM cells’ activation during recall stage

empirical results5
Empirical Results
  • Intersection case:
conclusion
Conclusion
  • A hierarchical spatio-temporal learning architecture
    • HMAX hierarchical feature construction and extraction
    • STM clustering by KFLANN
    • Sequence storage and retrieval by LTM cells.
  • Application in appearance-based topological localization
future directions
Future Directions
  • Automatic tolerance estimation
    • E.g. Signal-to-noise ratio figure of features [Liu&Starzyk 2008]
  • Hierarchical episodic memory which characterizes the interaction between STM and LTM
    • Other embodied intelligence components
    • Goal creation system [Starzyk 2008]
  • Application in other domains:
    • Human Action Recognition
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