Image recognition improvement

Bag-of-features (BoF) has already become one of the most popular models in image classification over the years. Applying spatial pyramid matching (SPM) together with BoF has shown to achieve much better classification accuracy and was widely used in image categorization. In recent years, spatial pyramid matching with sparse coding of SIFT (ScSPM) kernel evolved from SPM with sparse coding of SIFT features and max pooling approach greatly improve the image classification accuracy basing on common image databases testing. This report presents three new approaches as the optimization and supplementation of ScSPM kernel, which can be applied in the spatial matching process before max pooling in ScSPM. They are respective diagonal segmentation (DS) approach, max pooling with Gaussian parameter (GPMP) approach and overlapping segmentation (OS) approach. The experiments on classification accuracy of these approaches were implemented and further analysis was conducted to discuss the effect of each approach. Finally the result showed that the overlapping segmentation method can one step further increase the image classification accuracy on the basis of conventional ScSPM. Diagonal segmentation approach working together with the quadrate segmentation approach of conventional ScSPM also achieved better performance for some databases. However, it is just a start of ScSPM approach improvement. There is a board space in the advancement of existing approaches and algorithms of image recognition.Bachelor of Engineerin