Object detection based on visual memory: a feature learning and feature imagination process

ABSTRACT

Visual memory plays an important role for the human’s visual system to detect objects. The features of an object stored in the visual memory have much lower dimensions than the features contained within an image. We simulate the visual memory as a feature learning and feature imagination (FLFI) process to build an object detection algorithm. The method is constructed by a bottom-up feature learning and a top-down feature imagination. The proposed object detection method is tested using publicly available benchmark data sets, and the result indicates that it is fast and more robust.

KEYWORDS:

• Visual memory
• feature learning and feature imagination (FIFL)
• object detection
• top-down
• bottom-up

Acknowledgments

This work is supported by the Chinese Academy of Sciences under Project YZ201510 (Research equipment development project of the Chinese Academy of Sciences).

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the Research equipment development project of the Chinese Academy of Sciences [YZ201510].