A Free Software tool for Automatic Tuning of Segmentation Parameters SPT 3.0 Pedro Achanccaray, Victor Ayma, Luis Jimenez, Sergio Garcia, Patrick Happ, Raul Feitosa, Antonio Plaza Dept. of Electrical Engineering, Pontifical Catholic University of Rio de Janeiro, Brazil. Dept. of Technology of Computers and Communications, University of Extremadura, Spain.

Outline 1.Introduction 2.Software Description 3.Software Overview 4.Conclusions 2

Outline 1.Introduction 2.Software Description 3.Software Overview 4.Conclusions 3

1. Introduction “Segmentation subdivides an image into its constituent regions or objects…” – Gonzales & Woods,  There are many different approaches to accomplish this task. 4 Region Growing Random Fields K-means Normalized Cuts Naïve Bayes Thresholding Watershed Baatz & Schäpe Mean-Shift Fuzzy C-means Artificial Neural Networks Level Sets among others…

1. Introduction  Which segmentation algorithm is the best?  When does the segmentation outcome is good?  The segmentation outcome looks good on a set of images.  Use a metric for segmentation evaluation. 5 Region Growing Random Fields K-means Normalized Cuts Naïve Bayes Thresholding Watershed Baatz & Schäpe Mean-Shift Fuzzy C-means Artificial Neural Networks Level Sets among others…

1. Introduction  Which segmentation algorithm is the best?  When does the segmentation outcome is good?  The segmentation outcome looks good on a set of images.  Use a metric for segmentation evaluation. 6 How to find the “best” set of parameters? Manual Parameter TuningAutomatic Parameter Tuning Region Growing Random Fields K-means Normalized Cuts Naïve Bayes Thresholding Watershed Baatz & Schäpe Mean-Shift Fuzzy C-means Artificial Neural Networks Level Sets among others…

1. Introduction 7 where: : set of parameters. : optimal parameters.

1. Introduction  The objective is to: 8

Outline 1.Introduction 2.Software Description 3.Software Overview 4.Conclusions 9

2. SPT Description → a. SPT Previous Version2. SPT Description → b. SPT NewVersion 10 H Number of correct detection based on the percentage of overlapping. AFI Over-/under-segmentation by analyzing the overlapping area. SIAddresses the shape conformity. RI Ratio between pair of pixels that were correctly classified and the total pairs of pixels. F Measures the trade-off between Precision and Recall. CNumber of pixels of the intersection. RBSB Ratio between the number of pixels outside the intersection. MS Cluster-based approach focused on finding local extrema in the density function of a data set. CRF Estimates the conditional probability of a label given certain feature vector from the image. Gb Represents the image as a graph and dissimilarity between pixels as edges. GPS Used to solve optimization problems without restrictions and without the need of information about the function derivatives. MADS Proposed to solve nonlinear optimization problems; it has similar structure than GPS. NM Minimize the value of an n-dimensional function through the comparison of the objective function values at (n+1) vertices in a general simplex.

Outline 1.Introduction 2.Software Description 3.Software Overview 4.Conclusions 11

3. SPT 3.0 – Overview 12

4. SPT 3.0 – Results What we obtain with the tool… 13

Outline 1.Introduction 2.Software Description 3.Software Overview 4.Conclusions 14

5. Conclusions  Experiments demonstrated its practicability to find good parameter values for a segmentation algorithm given an input image and a set of reference segments.  The SPT3 was designed in a modular way, so that future extensions, such as the inclusion of new segmentation algorithms, can be easily incorporated in it.  SPT3 can be used as a standalone image segmentation tool. 15

A Free Software tool for Automatic Tuning of Segmentation Parameters SPT 3.0 Thank you for the attention… Victor Ayma