Red Eye Detection through Bag-of-Keypoints Classification

Abstract

Abstract.Red eye artifacts are a well-known problem in digital photography. Small compact devices and point-and-click usage, typical of non-professional photography, greatly increase the likelihood for red eyes to appear in acquired images. Automatic detection of red eyes is a very challenging task, due to the variability of the phenomenon and the general difficulty in reliably discerning the shape of eyes. This paper presents a method for discriminating between red eyes and other objects in a set of red eye candidates. The proposed method performs feature-based image analysis and classification just considering the bag-of-keypoints paradigm. Experiments involving different keypoint detectors/descriptors are performed. Achieved results are presented, as well as directions for future work.

Red-Eyes Removal through Cluster-Based Boosting on Gray Codes

“…Battiato et al [5] have proposed to deal with the problem of red eye detection by using the bag-of-keypoints paradigm. It involves extraction of local image features, quantization of the feature space into a codebook through clustering, and extraction of codeword distribution histograms.…”

A New Algorithm of Fully-automatic Red-eye Removal

“…There are different algorithms about red-eye removal such as the algorithms based on face detection, skin color feature and machine learning [1][2][3][4][5][6][7][8]. There are also some methods to directly locate the red-eye based on the construction of eyes [9][10][11][12]. Some existing software provides the redeye-removal function such as PHOTOSHOP, NEO IMAGING and ACDSEE, but they need to circle with the red-eye areas in advance by manual intervention, then correcting the red-eye by the function.…”