Image Matching for Individual Recognition with SIFT, RANSAC and MCL

Monitoring the whale individuals in the ocean is a current problem among conservationists. Biologists often use photos of whale caudal for this problem as it is the most discriminant pattern for distinguishing an individual whale from another, but it often requires laborious visual analysis. There was a challenge announced in the SeaCLEF of LifeCLEF campaign for automatic whale individual recognition based on visual contents. We elaborated a solution to compare the photos of individuals by SIFT features (as simple image representation) with spatial consistency refinement method RANSAC (based on a rotation and scale transformation model). After determining the similarity of every pair, to discover the wrong similarity values and correct them, a clustering method was applied on the similarity graph of the dataset.

[1]  Jean-Christophe Lombardo,et al.  Unsupervised Individual Whales Identification: Spot the Difference in the Ocean , 2016, CLEF.

[2]  Dávid Papp,et al.  Object Detection, Classification, Tracking and individual Recognition for Sea Images and Videos , 2016, CLEF.

[3]  S. Dongen.  A cluster algorithm for graphs , 2000 .

[4]  Hervé Glotin,et al.  LifeCLEF 2017 Lab Overview: Multimedia Species Identification Challenges , 2017, CLEF.

[5]  G LoweDavid,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004 .

[6]  Dávid Papp,et al.  SVM classification of moving objects tracked by Kalman filter and Hungarian method , 2015, CLEF.

[7]  Cordelia Schmid,et al.  Scale & Affine Invariant Interest Point Detectors , 2004, International Journal of Computer Vision.

[8]  Robert C. Bolles,et al.  Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography , 1981, CACM.

[9]  Florent Perronnin,et al.  Fisher Kernels on Visual Vocabularies for Image Categorization , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[10]  Matthieu Guillaumin,et al.  Segmentation Propagation in ImageNet , 2012, ECCV.

[11]  Dávid Papp,et al.  Viewpoints Combined Classification Method in Image-based Plant Identification Task , 2014, CLEF.

[12]  David G. Lowe,et al.  Object recognition from local scale-invariant features , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[13]  Christopher G. Harris,et al.  A Combined Corner and Edge Detector , 1988, Alvey Vision Conference.

[14]  Matthieu Guillaumin,et al.  ImageNet Auto-Annotation with Segmentation Propagation , 2014, International Journal of Computer Vision.