Visual Object Recognition in Diverse Scenes with Multiple Instance Learning

Visual object recognition is important to the robot industry and is a prerequisite for other robot functionalities, such as grasping and manipulation. Object representation and a learning technique are two indispensable parts for this demanding task while arbitrary object appearance and diverse scenes with cluttered background are two great challenges. However, compared with object representation, the learning technique is less developed to deal with these challenges. This paper extends the multiple instance learning (MIL) technique to the multi-class classification scenario and introduces this multi-class MIL framework to the object recognition domain for the first time. This framework is independent of object representation and is useful for object/background discrimination in unseen scenes. Preliminary experiments show that it compares favorably with the supervised learning approach which takes whole images as the classifier training input

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