Abstract
Student: Alejandro Nespereira
Advisors: Farzad Husain (Catchoom), Tomasz Adamek (Catchoom) and Xavier Giró-i-Nieto (UPC)
Program: Master in Computer Vision (Class of 2016)
This report explores the suitability of using a Siamese Convolutional Neural Network (CNN) for the task of false positive rejection. We present a Siamese CNN model trained with an in-house dataset of weakly textured objects. Our model is able to successfully assert the classification of an object detection pipeline with unseen new objects. Additionally, we also compare it with a hand-crafted method in order to compare its performance. We demonstrate the usage of our model by learning to discriminate between inter and intra object classes for a challenging dataset.
