1. Spatio-Temporal Sequence Learning of Visual Place Cells for Robotic Navigation presented by Nguyen Vu Anh date: 20 th July, 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 IJCNN, WCCI, Barcelona, Spain, 2010

2. Outline Introduction HMAX Feature Building and Extraction Spatio-Temporal Learning and Recognition Empirical Results Conclusion and future directions

3. 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

4. Introduction System Architecture Classifier Sequence Storage Symbol Quantization Feature Building and Extraction

5. 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)]

6. 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:

7. HMAX Feature Building and Extraction Prototypes Ref: Riesenhuber & Poggio (1999), Serre et al (2007) Spatial Invariance Processing Dot-Product Matching

8. Spatio-Temporal Learning Architecture STM Structure: –Quantization of input using KFLANN with vigilance ρ See: Tay, Zurada,Wong and Xu, TNN, 2007

9. Spatio-Temporal Learning Architecture STM Structure: See: Tay, Zurada,Wong and Xu, TNN, 2007

10. 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.

11. Spatio-Temporal Learning Architecture LTM Cell Structure: Dual Neurons – STM Primary Neurons – Primary Excitation

12. Spatio-Temporal Learning Architecture Storage –One-shot learning Recognition Input feature vector Primary Excitation Computation Dual Neurons Update – Evidence Accumulation Output Matching Score from the last DN

13. 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

14. 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.

15. Empirical Results Table: LTM listing with training set S

16. Empirical Results Accuracy without threshold Accuracy with threshold ө=0.4 Robust testing: missing elements

17. Empirical Results Figure: LTM cells’ activation during recall stage

18. Empirical Results Intersection case:

19. 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

20. 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

21. Thank you!