Qualification and Quantification on Viewpoint Biases in Large Scale Image Datasets for General Object Recognition
Yue Qiu, Yutaka Satoh, Ryota Suzuki, Hirokatsu Kataoka, Kenji Iwata · Journal of the Japan Society for Precision Engineering · 2019
While recognizing an image taken from an arbitrary angle in a real environment, it may be challenging to obtain a good recognition result on occasion. That is, the recognition accuracy is affected by changing the viewpoint (object viewing direction). This phenomenon could come from the fact that people photograph target objects on well explainable aspect. It has been known empirically that the recognition rate varies depending on the viewpoint so far, but this topic remains underdeveloped. Therefore, in this research, we aim to clarify and visualize the existence of viewpoint biases in existing large object recognition image datasets. We also quantify the viewpoint biases by a definition of viewpoint bias index.