Improving Classification Accuracy of Image Categories Using Local Descriptors with Supplemental Information

Abstract

In this paper we address the problem of image classification by embedding the spatial information into the local descriptor. In our method, we directly concatenate (x, y) coordinates of an image into the original feature vector. This simple idea can perform well in the object category classification even though the feature vector size is almost the same as the conventional approach. Results are reported for classification of the Caltech-101 dataset and our methods are found to produce consistently better results compared with traditional Bag-of-Features approaches in all experiments.