Improving Landmark Recognition Using Saliency Detection and Feature Classification

With increasing tourism and democratization of data, image landmark recognition has been one of the most sought-after classification challenges in the field of vision and perception. After so many years of generic classification of buildings and monuments from images, people are now focusing on fine-grained problems—recognizing the category of each building or monument and indexing information to it. In this chapter, we propose an ensemble network for the classification of Indian landmark images. To this end, the proposed method gives a robust classification by ensembling the predictions from the Graph-Based Visual Saliency (GBVS) network along with supervised feature-based classification algorithms such as K-nearest neighbor (kNN) and Random Forest. The final architecture is an adaptive learning of all the mentioned networks. The proposed network produces a decent score to eliminate false category cases. Evaluation of the proposed model was done on a new dataset, which involves challenges such as landmark clutter, variable scaling, and partial occlusion.