Category-level object detection and image classification - Chia, Alex Yong Sang (2010)
Automatic recognition of object categories from complex real-world images is an exciting problem in computer vision. While humans can perform recognition tasks effortlessly and proficiently, replicating this recognition ability of humans in machines is still an incredibly difficult problem. On the other hand, successful automatic recognition technology will have significant and mostly positive impact in a plethora of important application domains like image retrieval, visual surveillance and automotive safety systems. This dissertation addresses two main goals of recognition: image classification and object detection. Image classification seeks to separate images which contain an object category from other images, where the focus is on identifying the presence or absence of an object category in an image. Object detection concerns the identification and localization of object instances of a category in an image, where the goal is to localize all instances of that category from the image.