Learning Visual Scene Attributes
نویسنده
چکیده
Take a look around you. How would you describe your surroundings to best give an idea of what everything looks like to someone not there? Maybe you will give a category to the scene, say, ‘bedroom’. You might try to list some of the objects around you, like ‘bed’, ‘lamp’, and ‘desk’. Or perhaps you’ll describe it with adjectives like ‘indoors’, ‘cozy’, and ‘cluttered’. In computer vision, (or more specifically, in scene understanding), the most effective way to describe a visual scene is also a major question.
منابع مشابه
Cue Fusion using Information Theoretic Learning
In order to segment independently moving objects (IMO) in a visual scene, neurons in the higher processing stages of the visual cortex are able to fuse visual attributes (cue fusion). The modeling of cue fusion is the objective of the predoctoral project and will be developed in the context of Information Theoretic Learning.
متن کاملRecognizing Material Properties from Images
Humans implicitly rely on properties of the materials that make up ordinary objects to guide our interactions. Grasping smooth materials, for example, requires more care than rough ones, and softness is an ideal property for fabric used in bedding. Even when these properties are not purely visual (softness is a physical property of the material), we may still infer the softness of a fabric by l...
متن کاملPlaces: An Image Database for Deep Scene Understanding
The rise of multi-million-item dataset initiatives has enabled data-hungry machine learning algorithms to reach nearhuman semantic classification at tasks such as object and scene recognition. Here we describe the Places Database, a repository of 10 million scene photographs, labeled with scene semantic categories and attributes, comprising a quasi-exhaustive list of the types of environments e...
متن کاملZero-shot Object Prediction using Semantic Scene Knowledge
This work focuses on the semantic relations between scenes and objects for visual object recognition. Semantic knowledge can be a powerful source of information especially in scenarios with few or no annotated training samples. These scenarios are referred to as zero-shot or fewshot recognition and often build on visual attributes. Here, instead of relying on various visual attributes, a more d...
متن کاملOnline generation of scene descriptions in urban environments
The ability to extract a rich set of semantic workspace labels from sensor data gathered in complex environments is a fundamental prerequisite to any form of semantic reasoning in mobile robotics. In this paper we present an online system for the augmentation of maps of outdoor urban environments with such higher-order, semantic labels. The system employs a shallow supervised classification hie...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2013