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首页> 外文期刊>Neural Networks: The Official Journal of the International Neural Network Society >Interactive image data labeling using self-organizing maps in an augmented reality scenario.
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Interactive image data labeling using self-organizing maps in an augmented reality scenario.

机译：在增强现实场景中使用自组织地图进行交互式图像数据标记。

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摘要

We present an approach for the convenient labeling of image patches gathered from an unrestricted environment. The system is employed for a mobile Augmented Reality (AR) gear: while the user walks around with the head-mounted AR-gear, context-free modules for focus-of-attention permanently sample the most 'interesting' image patches. After this acquisition phase, a Self-Organizing Map (SOM) is trained on the complete set of patches, using combinations of MPEG-7 features as a data representation. The SOM allows visualization of the sampled patches and an easy manual sorting into categories. With very little effort, the user can compose a training set for a classifier, thus, unknown objects can be made known to the system. We evaluate the system for COIL-imagery and demonstrate that a user can reach satisfying categorization within few steps, even for image data sampled from walking in an office environment. (An abbreviated version of some portions of this article appeared in [Bekel, H., Heidemann, G., &Ritter, H. (2005). SOM Based Image Data Structuring in an Augmented Reality Scenario. In Proceedings of the International Joint Conference on Neural Networks, Montreal, Canada.], as part of the IJCNN 2005 conference proceedings, published under the IEEE copyright).
机译：我们提出了一种方便地标记从不受限制的环境中收集的图像补丁的方法。该系统用于移动式增强现实（AR）装置：当用户带着头戴式AR齿轮行走时，用于关注焦点的上下文无关模块永久采样最“有趣”的图像块。在此获取阶段之后，使用MPEG-7功能的组合作为数据表示，在完整的补丁集上训练自组织映射（SOM）。 SOM可以可视化采样的补丁，并可以轻松地手动分类。用户只需很少的精力就可以为分类器编写训练集，从而使系统知道未知对象。我们评估了用于COIL图像的系统，并证明了用户即使在办公室环境中步行获得的图像数据，也可以在几步之内达到满意的分类。 （本文的某些部分的缩写形式出现在[Bekel，H.，Heidemann，G.，＆Ritter，H.（2005）。在增强现实场景中基于SOM的图像数据结构。国际联合会议论文集神经网络，加拿大蒙特利尔。]，作为IJCNN 2005会议论文集的一部分，以IEEE版权出版。

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