Aim of the project Take your image Submit it to the search engine Similar images are found Name of the building appears

Landmark Locations

Recognition of Buildings Using SIFT (ROBUST)

Solution overview Image representation by SIFT keypoints Sky and hedge detection Non object features are removed Similarity matching of features with database

Properties of SIFT features Scale invariance Rotation invariance Robustness to illumination changes Robustness to reasonable perspective transformation

SIFT Features

SIFT features computation Keypoint detection (local minima and maxima) Orientation of intensity gradients in the neighborhood Detection of significant orientations Histogram of orientations

Keypoint Refinement Sky detection (fast) Hedge detection (slower) Thresholding, morphological operations Applied to database and query images Hedge detection (slower) Hough transformation Closing morphology operation Applied to database images

Keypoints refinement

Similarity of features Matching between keypoints Take only reliable matches Similarity: number of matched keypoints

Distance Matrix

Results SIFT Features outperformed PCA and Color Histogram Comparison Leave-one-out Validation First match correct: 95.5% First or second match correct: 98.5%

Demo: Obscure Angle

Demo: Rotated, Reflected

Demo: People in Image

Demo: Night

Demo: Distant, Busy Scene

Demo: Team