Ertuğrul BAYRAKTAR, Pınar BOYRAZ

Analysis of feature detector and descriptor combinations with a localization experiment for various performance metrics

The purpose of this study is to provide a detailed performance comparison of feature detector/descriptor methods, particularly when their various combinations are used for image-matching. The localization experiments of a mobile robot in an indoor environment are presented as a case study. In these experiments, 3090 query images and 127 dataset images were used. This study includes five methods for feature detectors (features from accelerated segment test (FAST), oriented FAST and rotated binary robust independent elementary features (BRIEF) (ORB), speeded-up robust features (SURF), scale invariant feature transform (SIFT), and binary robust invariant scalable keypoints (BRISK)) and five other methods for feature descriptors (BRIEF, BRISK, SIFT, SURF, and ORB). These methods were used in 23 different combinations and it was possible to obtain meaningful and consistent comparison results using the performance criteria defined in this study. All of these methods were used independently and separately from each other as either feature detector or descriptor. The performance analysis shows the discriminative power of various combinations of detector and descriptor methods. The analysis is completed using five parameters: (i) accuracy, (ii) time, (iii) angle difference between keypoints, (iv) number of correct matches, and (v) distance between correctly matched keypoints. In a range of 60◦ , covering five rotational pose points for our system, the FAST-SURF combination had the lowest distance and angle difference values and the highest number of matched keypoints. SIFT-SURF was the most accurate combination with a 98.41% correct classification rate. The fastest algorithm was ORB-BRIEF, with a total running time of 21,303.30 s to match 560 images captured during motion with 127 dataset images.

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[1] Lowe DG. Distinctive image features from scale-invariant keypoints. Int J Comput Vision 2004; 2: 91-110.
[2] Bay H, Bay A, Tuytelaars T, van Gool L. Speeded-up robust features (SURF). Comput Vis Image Und 2008; 3: 346-359.
[3] Calonder M, Lepetit V, Strecha C, Fua P. BRIEF: Binary robust independent elementary features. In: 11th European Conference on Computer Vision Proceedings Part IV; 511 September 2010; Heraklion, Crete, Greece. Berlin, Germany: Springer-Verlag Berlin Heidelberg. pp. 778-792.
[4] Rublee E, Rabaut V, Konolige K, Bradski G. ORB: An efficient alternative to SIFT or SURF. In: IEEE International Conference on Computer Vision; 613 November 2011; Barcelona, Spain. New York, NY, USA: IEEE. pp. 2564-2571.
[5] Rosten E, Drummond T. Fusing points and lines for high performance tracking. In: IEEE International Conference on Computer Vision; 1721 October 2005; Beijing, China. New York, NY, USA: IEEE. pp. 1508-1515.
[6] Rosten E, Drummond, T. Machine learning for high-speed corner detection. In: 9th European Conference on Computer Vision Proceedings; 713 May 2006; Graz, Austria. Berlin, Germany: Springer-Verlag Berlin Heidelberg. pp. 430-443.
[7] Leutenegger S, Chli M, Siegwart, RY. BRISK: Binary robust invariant scalable keypoints. In: IEEE International Conference on Computer Vision; 613 November 2011; Barcelona, Spain. New York, NY, USA: IEEE. pp. 2548-2555.
[8] Remondino F. Detectors and descriptors for photogrammetric applications. ISPRS J Photogramm 2006; 3: 49-54.
[9] Dahl L, Aanæs AH, Pedersen KS. Finding the best feature detector-descriptor combination. In: International Conference on 3D Imaging, Modeling, Processing, Visualization and Transmission; 1619 May 2011; Hangzhou, China. New York, NY, USA: IEEE. pp. 318-325.
[10] Matas J, Chum O, Urban M, Pajdla T. Robust wide-baseline stereo from maximally stable extremal regions. Image Vision Comput 2004; 10: 761-767.
[11] Tola E, Lepetit V, Fua P. DAISY: An efficient dense descriptor applied to wide-baseline stereo. IEEE T Pattern Anal 2010; 5: 815-830.
[12] Figat J, Kornuta T, Kasprzak W. Performance evaluation of binary descriptors of local features. In: Leszek JC, Ryszard K, Bok-Suk S, Konrad W, editors. Computer Vision and Graphics. Warsaw, Poland: Springer International Publishing, 2014. pp. 187-194.
[13] Mikolajczyk K, Schmid C. A performance evaluation of local descriptors. IEEE T Pattern Anal 2005; 10: 1615-1630.
[14] Harris C, Stephens M. A combined corner and edge detector. In: Alvey Vision Conference; 31 August2 September 1988; Manchester, UK. pp. 147-151.
[15] Mikolajczyk K, Schmid C. Comparison of affine-invariant local detectors and descriptors. In: 12th European Signal Processing Conference; 610 September 2004; Vienna, Austria. New York, NY, USA: IEEE. pp. 1729-1732.
[16] Iscen A, Tolias G, Gosselin PH, J´egou H. A comparison of dense region detectors for image search and fine-grained classification. IEEE T Image Process 2015; 8: 2369-2381.
[17] Guclu O, Can AB. A comparison of feature detectors and descriptors in RGB-D SLAM methods. In: Kamel M, Campilho A, editors. Image Analysis and Recognition. Niagara Falls, ON, Canada: Springer International Publishing, 2015. pp. 297-305.
[18] Hartmann J, Kl¨ussendorff JH, Maehle E. A comparison of feature descriptors for visual SLAM. In: European Conference on Mobile Robots (ECMR); 2527 September 2013; Barcelona, Spain. New York, NY, USA: IEEE. pp. 56-61.
[19] Chao J, Al-Nuaimi A, Schroth G, Steinbach E. Performance comparison of various feature detector-descriptor combinations for content-based image retrieval with JPEG-encoded query images. In: IEEE 15th International Workshop on Multimedia Signal Processing (MMSP); 30 September 2 October 2013; Pula, Croatia. New York, NY, USA: IEEE. pp. 29-34.
[20] Mikolajczyk K, Tuytelaars T, Schmid C, Zisserman A, Matas J, Schaffalitzky F, Kadir T, Van Gool L. A comparison of affine region detectors. Int J Comput Vision 2005; 1-2: 43-72.
21 Boyraz P, Yigit CB, Bicer HO. UMAY: A modular humanoid platform for education and rehabilitation of children with autism spectrum disorders. In: 9th Asian Control Conference (ASCC); 2326 June 2013; ˙Istanbul, Turkey. New York, NY, USA: IEEE. pp. 1-6.