In image recognition, combining local and global information is crucial to improve accuracy. This article compares six different strategies for combining these two types of information. The strategies are tested on the ImageNet dataset, and their performance is evaluated.
Strategies for Combining Local and Global Information
- MIN (Maximum Inliers): This strategy selects the most similar images based on a distance metric, such as Euclidean distance.
- C10 (Centroid + 10 neighbors): This strategy combines local information by averaging the centroid of an image with the features of its 10 nearest neighbors.
- Fashion Params (Fashion + Parameters): This strategy combines local and global information by adding style parameters to the feature space, which captures the variations in image appearance due to lighting, pose, and other factors.
- L-G (Local-Global): This strategy combines local and global information by using a weighted sum of the two types of features. The weights are learned during training.
- Residual: This strategy adds the residual between the local and global features to improve the performance.
- Concat+Reduce (Concatenate + Reduce): This strategy combines local and global information by concatenating their feature vectors and then reducing the dimensionality using PCA or LLE.
Performance Evaluation
The six strategies are evaluated on the ImageNet dataset, which consists of 1.2 million images across 200 classes. The performance is measured in terms of accuracy, and the results show that the best-performing strategy is Concat+Reduce, followed closely by L-G. The other strategies perform relatively poorly.
Visualization
To provide a better understanding of the strategies, visualizations are included for each one. These visualizations show how the different strategies combine local and global information to form the final feature vector.
Conclusion
In conclusion, this article compares six different strategies for combining local and global information in image recognition. The results show that Concat+Reduce and L-G perform best, while the other strategies struggle. These findings provide insights into how to improve image recognition systems by effectively combining local and global information.
