Image description with a goal: Building efficient discriminating expressions for images
Amir Sadovnik, Yi-I Chiu, Noah Snavely, Shimon Edelman, Tsuhan Chen · 2012
Many works in computer vision attempt to solve different tasks such as object detection, scene recognition or attribute detection, either separately or as a joint problem. In recent years, there has been a growing interest in combining the results from these different tasks in order to provide a textual description of the scene. However, when describing a scene, there are many items that can be mentioned. If we include all the objects, relationships, and attributes that exist in the image, the description would be extremely long and not convey a true understanding of the image. We present a novel approach to ranking the importance of the items to be described. Specifically, we focus on the task of discriminating one image from a group of others. We investigate the factors that contribute to the most efficient description that achieves this task. We also provide a quantitative method to measure the description quality for this specific task using data from human subjects and show that our method achieves better results than baseline methods.