- 學位論文
Rotation, Translation, and Scale Invariant Bag of Feature based on Feature Density
Rotation, Translation, and Scale Invariant Bag of Feature based on Feature Density
並列摘要
In human vision, people can easily recognize object in image with any size, location at any position, at any angle, and with complicated background. But in computer vision, it is hard to achieve image recognition with such invariance. Spatial Pyramid Matching (SPM) has excellent performance on computer vision applications. However, SPM still meets the difficulty when the position of object changes in images. In recent year, researchers try to find a robust representation. For example, translation invariant, rotation invariant, and scale invariant features. There are works trying to solve this issue. However, they just deal with one of three invariants respectively. It lacks a robust representation that can handle three invariant simultaneously. In our work, we aim to develop a robust feature that achieves translation, rotation, and scale invariant simultaneously. To handle this problem, we propose a novel method named Block Based Integral Image to search the densest region of features and constraint the region size similar to a predefined region size, and further find the approximated center of object in image. Then, we apply SPR by replacing the image center with the approximated object center to handle translation and rotation invariance problem. After that, we use histogram equalization to adjust captured representation for scale invariant. After the adjustment, a robust representation can be obtained to handle translation, rotation, and scale invariance simultaneously. Finally, we verify our system on different datasets on image classification task. Experimental results show that our system indeed can deal with translation, rotation, and scale invariant simultaneously and achieve higher accuracy than the previous methods.