Large Scale Object Detection

This thesis focuses on the problem of large scale visual object detection and classification in digital images. A new type of image features that are derived from state-of-the-art convolutional neural networks is proposed. It is further shown that the newly proposed image signatures bare a strong resemblance to the Fisher Kernel classifier, that recently became popular in the object category retrieval field. Because this new method suffers from having a large memory footprint, several feature compression / selection techniques are evaluated and their performance is reported. The result is an image classifier that is able to surpass the performance of the original convolutional neural network, from which it was derived. The new feature extraction method is also used for the object detection task with similar results.This thesis focuses on the problem of large scale visual object detection and classification in digital images. A new type of image features that are derived from state-of-the-art convolutional neural networks is proposed. It is further shown that the newly proposed image signatures bare a strong resemblance to the Fisher Kernel classifier, that recently became popular in the object category retrieval field. Because this new method suffers from having a large memory footprint, several feature compression / selection techniques are evaluated and their performance is reported. The result is an image classifier that is able to surpass the performance of the original convolutional neural network, from which it was derived. The new feature extraction method is also used for the object detection task with similar results