Lazy Lazy PhotographerPhotographer

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Motivation

‧ A lazy photographer

‧ after traveling, we want to see photos as soon as possible.

Four approaches

‧ remove over exposure

‧ remove blur image

‧ remove (denote) duplication

‧ clustering the photos by scene

Over exposure

‧ use Lab color space

‧ split photo to blocks

‧ use L value and the distance ab to (0,0)

‧ set various thresholds to detect‧ “Correcting Over-Exposure in Photographs “ (must read)

Blur

‧ deal with vibration or defocused

‧ Use gradient magnitude + gradient

direction as a feature vector

‧ take 100 blur photos and 100 non-blur to train a model by SVM‧ “Blurred Image Detection and Classification” (must read)

Duplication

‧ compare photo with SIFT feature

‧ compare with the next n photos

Clustering

‧ based on SIFT feature

‧ union similar photos

‧ set thresholds to detect

Result

‧ dataset : 120 photos with 23 over exposure

43 blur photos

‧ dataset2 : 60 photos in a single trip

DEMO TIMEDEMO TIME

Result (cont.)

‧ Blur detection Recall 83.72% (36/43) Precision 76.60% (36/47)

‧ Over exposure Recall 82.60% (19/23) Precision 79.17% (19/24)

Conclusion & future

‧ Auto photo adjustment based on our system, fast and convenient

‧ replace or correct over exposure parts

‧ deblur

‧ user-friendly UI

Thank you!Thank you!