Nov. 11
2 pm – 3 pm
2116 Hornbake Bldg, South Wing

Speaker:
Mercedes Torres, PhD  
As a post-­‐doctoral student at the University of Nottingham, she is focused on interdisciplinary research, specifically in the area of Fine-­‐Grained Visual Categorization, Image Processing and Analysis, and Machine Learning. She has designed and developed an image annotation framework for Phase 1 habitat classification in ground-­‐taken photographs.

Abstract:
Currently, habitat classification (the process of mapping an area with the habitats present on it) is carried out by human surveyors.This is expensive, time consuming, laborious and subjective. What I have done is develop the first complete automatic alternative for the Phase 1 classification Scheme, widely used in the UK. The problems itself is quite complicated, giving the semantic similarities between the classes I have to recognize. I have approached habitat classification as an image annotation problem and created a complete framework for it, composed of 5 elements: the source data, features extracted from these data (low and medium), a novel machine learning classifier called Random Projection Forests and a location-­‐ based voting system for my classifier. Moreover, I have used a novel source of information as the input of this framework: ground-­‐taken geo-­‐referenced photographs (which can be photographs taken with a mobile phone). Current state-­‐of-­‐the-­‐art normally uses remote-­‐sensed imagery, but this is not detailed enough to distinguish between vegetation species, etc.,so ground-­‐taken photos are actually better alternatives. Additionally, I have created a new ensemble classifier, called Random Projection Forests and based on Random Forests, but much more efficient and accurate. Results show that my complete framework can successfully classify 7 out of the 10 main classes of Phase 1, which is quite good considering that this type of work has never been done before with the type of data I am using and the approach I have chosen.