1 Leaf Classification from Boundary Analysis Anne Jorstad AMSC 663 Project Proposal Fall 2007 Advisor: Dr. David Jacobs, Computer Science

2 Background Electronic Field Guide for Plants University of Maryland Columbia University National Museum of Natural History Smithsonian Institution Project in development over 4 years

3 Background  Current System: Inputs photo of leaf on plain background Segments leaf from background Compares leaf to all leaves in database, using global shape information Returns images of closest matches to the user

4 Background Sean White, Dominic Marino, Steven Feiner. Designing a Mobile User Interface for Automated Species Identification. Columbia University, 2007.

5 Background  All leaves assumed to be from woody plants the Baltimore- Washington, DC area  245 species, 8000 images  The proof of concept has been implemented successfully

6 Proposal  Current System: All shape information is compared at a global level, no specific consideration of edge types  My Project: Incorporate local boundary information to complement existing system

7 Proposal Leaf edges: smooth serrated serrated, finer teeth “double-toothed” wavy lobed and serrated

8 Proposal Specifics  Start with boundary curves as discrete points (already have this data with good accuracy)  Represent as, to use 1-D techniques  Classify!

9 Method 1: Harmonic Analysis  Harmonic Analysis Decompose boundary into wavelet basis Different families of species have distinct serration patterns in the frequency domain What wavelet basis to choose?

10 Aside: What is a wavelet?  Fourier Transform: decomposes a function into frequency components  Wavelet Transform: similar to Fourier, but with quickly decaying or compactly supported basis functions  good for feature detection

11 Method 1: Harmonic Analysis  Think of the boundary as a texture  Several Computer Vision algorithms exist for classifying textures  Example: Describe texture in terms of a set of fundamental features or patterns (sound like a wavelet basis?), search for them throughout the image

12 Method 2: Inner-Distance  “Inner-Distance” on multiple scales Measures the shortest distance between two points on a path contained entirely within a figure Good for detecting similarities between deformable structures

13 Method 2: Inner-Distance  The inner-distance has been successfully applied in several situations  Used already as part of the global classification  New: sample points on several scales and look for shape discrepancies not previously measured

14 Method 2: Inner-Distance  Examining inner-distances over a hierarchy of scales will capture new local information Large scale: similar inner-distances Small scale: distinct inner-distances

15 Method 3: Convexity  A serrated leaf is much less convex than a smooth one; use convexity measure as a pre-processing classification tool  May not prove useful, but might be worth exploring

16 Method 3: Convexity  Several ways to assign a convexity number to a shape: etc. object ConvexHull(object)

17 Algorithm Verification  Create artificial “leaves” with known properties  Prove algorithm correctness on these simple known cases

18 Algorithm Verification  Run new algorithm on current data sets Demonstrate “reasonable” classification accuracy for relevant examples Global information not considered, so expect that not all distinguishing features will be recognized

19 Algorithm Verification  Incorporate into existing system  Ideally: Provide classification results independent from current results, so together a better overall classification is achieved

20 Specifications  Current system: MATLAB and C  My contribution: mostly MATLAB Image Processing Toolbox Wavelet Toolbox

21 Specifications  End product to run on portable computer Code must run quickly on a small processor Development and testing from PC

22 References  “A New Convexity Measure for Polygons”. Jovisa Zunic, Paul L. Rosin. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 26, no. 7, July  “Contour and Texture Analysis for Image Segmentation”. Jitendra Malik, Serge Belongie, thomas Leung, Jainbo Shi. International Journal of Computer Vision, vol. 34, no. 1, July  “Designing a Mobile User Interface for Automated Species Identification”. Sean White, Dominic Marino, Steven Feiner. Proceedings of the SIGCHI, April  “First Steps Toward an Electronic Field Guide for Plants”. Gaurav Agarwal, Haibin Ling, David Jacobs, Sameer Shirdhonkar, W. John Kress, Rusty Russell, Peter Belhumeur, Nandan Dixit, Steve Feiner, Dhruv Mahajan, Kalyan Sunkavalli, Ravi Ramamoorthi, Sean White. Taxon, vol. 55, no. 3, Aug  “Using the Inner-Distance for Classification of Articulated Shapes”. Haibin Ling, David W. Jacobs. IEEE Conference on Computer Vision and Pattern Recognition, vol. II, June 2005.

23 Questions? Comments?