  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 focussing upon fine-grained problems - recognizing the category of each
building or monument. We proposed an ensemble network for the purpose of
classification of Indian Landmark Images. To this end, our method gives robust
classification by ensembling the predictions from Graph-Based Visual Saliency
(GBVS) network alongwith supervised feature-based classification algorithms
such as kNN and Random Forest. The final architecture is an adaptive learning
of all the mentioned networks. The proposed network produces a reliable score
to eliminate false category cases. Evaluation of our model was done on a new
dataset, which involves challenges such as landmark clutter, variable scaling,
partial occlusion, etc.
