Scene Recognition by Combining Local and Global Image Descriptors
This work provides a practical implementation for scene recognition, but it is incremental as it combines existing methods without introducing new paradigms.
The authors tackled scene recognition by combining local DAISY and global HOG descriptors in a pipeline, achieving an average accuracy of 76.4% on the fifteen scene categories dataset with a 40%-60% train-test split.
Object recognition is an important problem in computer vision, having diverse applications. In this work, we construct an end-to-end scene recognition pipeline consisting of feature extraction, encoding, pooling and classification. Our approach simultaneously utilize global feature descriptors as well as local feature descriptors from images, to form a hybrid feature descriptor corresponding to each image. We utilize DAISY features associated with key points within images as our local feature descriptor and histogram of oriented gradients (HOG) corresponding to an entire image as a global descriptor. We make use of a bag-of-visual-words encoding and apply Mini- Batch K-Means algorithm to reduce the complexity of our feature encoding scheme. A 2-level pooling procedure is used to combine DAISY and HOG features corresponding to each image. Finally, we experiment with a multi-class SVM classifier with several kernels, in a cross-validation setting, and tabulate our results on the fifteen scene categories dataset. The average accuracy of our model was 76.4% in the case of a 40%-60% random split of images into training and testing datasets respectively. The primary objective of this work is to clearly outline the practical implementation of a basic screne-recognition pipeline having a reasonable accuracy, in python, using open-source libraries. A full implementation of the proposed model is available in our github repository.