Landmark Scene Classification Using Vision Transformer

In building the navigation software application, the Landmark plays an important factor in visual place recognition. There is major problem for performing the classification to visual recognition, due to change of environments like summer, winter, rainy season the place changes. To overcome the problem, the paper proposes a vision transformer as classification for place recognition. In vision transformer the first stage is considerations images are divided into various parts, then it converts into sequence embedding by moving it. To keep information at correct position, the embedding position is used in the parts and the sequence result is sent to various multihead attention layers for final result generation. For validating the proposal, we performed the vision transformer model on the two different datasets such as paris and oxford datasets. The result obtained by performing the vision transformer is demonstrated the other existing model in terms of accuracy in two datasets the given values are 90.63% and 87.30% respectively.