1. Biologically Inspired Vision-based Indoor Localization Zhihao Li, Ming Yang iLab@TongjiU

2. Motivation Human vision is the gold standard for most vision tasks. W. Choi, Y. -W. Chao, C. Pantofaru, S. Savarese. "Understanding Indoor Scenes Using 3D Geometric Phrases" in CVPR, 2013.

3. Indoor Environment GLOBAL FEATURESLOCAL FEATURES A. Quattoni and A. Torralba. Recognizing indoor scenes. In CVPR, 2009.

4. Biological Plausible Methods Visual Attention Mechanism: Saliency Map Gist of a scene

5. Visual Attention A neuromorphic model that simulates which elements of a visual scene are likely to attract the attention of human observers. Given an image or video sequence, the model computes a saliency map, which topographically encodes for conspicuity (or ``saliency'') at every location in the visual input. The model predicts human performance on a number of psychophysical tasks. L. Itti, C. Koch, Computational Modelling of Visual Attention, Nature Reviews Neuroscience, Vol. 2, No. 3, pp. 194-203, Mar 2001.

6. Computational Visual Attention Calculate Saliency Map L. Itti, C. Koch, E. Niebur, A Model of Saliency-Based Visual Attention for Rapid Scene Analysis, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 20, No. 11, pp. 1254-1259, Nov 1998.

7. Gist of a Scene A simple context-based scene recognition algorithm using a multiscale set of early-visual features, which capture the GIST of the scene into a low- dimensional signature vector. C. Siagian, L. Itti, Rapid Biologically-Inspired Scene Classification Using Features Shared with Visual Attention, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 29, No. 2, pp. 300-312, Feb 2007.

8. Our Approach

9. Current Progress Data set: 8 rooms (labs, offices), 62 positions, 1988 images extracted from 62 videos with average length of 30 seconds Test set: 398 images Training set: 1590 images Results (With top 10 nearest neighbors of each test image, these percentages show rate of at least 1 training image being correctly located at the same room): LDA: 96.5% (n_components = 62) No decomposition: 92.0% PCA: 91.7% (n_components = 0.97)

10. Q&A