Object Recognition and Localization Via Spatial Instance Embedding

—We propose an approach for improving object recognition and localization using spatial kernels together with instance embedding. Our approach treats each image as a bag of instances (image features) within a multiple instance learning framework, where the relative locations of the instances are considered as well as the appearance similarity of the localized image features. The introduced spatial kernel augments the recognition power of the instance embedding in an intuitive and effective way, providing increased localization performance. We test our approach over two object datasets and present promising results. Keywords-object recognition; object localization; multiple instance learning;

• Type: Conference
• Year: 2010
• Where: ICPR
• Authors: Nazli Ikizler Cinbis, Stan Sclaroff
• Keywords: Computer Vision, ICPR 2010, Instance Learning Framework, Multiple Instance Learning, Spatial Kernel