Object recognition is a central problem in computer vision which deals with both localizing and identifying objects in images. Object proposals have recently become an important part of the object recognition process. Object proposals are algorithms used for localizing objects in images. This thesis is a study in object proposals and is composed of three parts. First, we present a new data-driven approach for generating object proposals. Second, we release a MATLAB library which can be used to generate object proposals using all the existing algorithms. The library can also be used for evaluating object proposals using the three most commonly used metrics. Finally, we identify previously unnoticed bias in the existing protocol for evaluating object proposals and propose ways to alleviate this bias. / Master of Science

Object region detection plays a vital role in many domains ranging from self-driving cars to lane detection, which heavily involves the task of object detection. Improving the performance of object region detection approaches is of great importance and therefore is an active ongoing research in Computer Vision. Traditional sliding window paradigm has been widely used to identify hundreds of thousands of windows (covering different scales, angles, and aspect ratios for objects) before the classification step. However, it is not only computationally expensive but also produces relatively low accuracy in terms of the classifier output by providing many negative samples. Object detection proposals, as discussed in detail in [19, 20], tackle these issues by filtering the windows using different features in the image before passing them to the classifier. This filtering process helps to control the quality as well as the quantity of the windows. EdgeBox is one of the most effective proposal detection approaches that focuses on the presence of dense edges in an image to identify quality proposal windows. This thesis proposes an innovative approach that improves the accuracy of the EdgeBox approach. The improved approach uses both the color properties and the corner information from an image along with the edge information to evaluate the candidate windows. We also describe two variations of the proposed approach. Our extensive experimental results on the Visual Object Classification (VOC) [29,30] dataset clearly demonstrate the effectiveness of the proposed approach together with its two variances to improve the accuracy of the EdgeBox approach.