Building of classifiers based on conjugation indices

Fursov, V. A.; Kulagina, Irina; Kozin, Nikita

doi:10.3103/s1060992x07030034

Cited by 7 publications

(1 citation statement)

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“…In the study by Fursov, 23 the use of a conjugation index was offered. In the work by Fursov et al, 24,25 approach was shown to be effective for object recognition where patterns have significant variability in the class. In particular, in the articles by Kozin and Fursov 26 and Fursov and Kozin, 27,28 examples of successfully applying the distance function for face recognition were given.…”

Section: Problem Statementmentioning

confidence: 99%

Support subspaces method for synthetic aperture radar automatic target recognition

Fursov¹,

Zherdev²,

Kazanskiy

2016

International Journal of Advanced Robotic Systems

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This article offers a new object recognition approach that gives high quality using synthetic aperture radar images. The approach includes image preprocessing, clustering and recognition stages. At the image preprocessing stage, we compute the mass centre of object images for better image matching. A conjugation index of a recognition vector is used as a distance function at clustering and recognition stages. We suggest a construction of the so-called support subspaces, which provide high recognition quality with a significant dimension reduction. The results of the experiments demonstrate that the proposed method provides higher recognition quality (97.8%) than such methods as support vector machine (95.9%), deep learning based on multilayer auto-encoder (96.6%) and adaptive boosting (96.1%). The proposed method is stable for objects processed from different angles.

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Section: Problem Statementmentioning

confidence: 99%