A Novel Distant Target Region Detection Method Using Hybrid Saliency-Based Attention Model Under Complex Textures

JaepilKo1 and Kyung Joo Cheoi2

  1. Department of Computer Engineering, Kumoh National Institute of Technoligy
    Daehak-ro 61, Gumi-si, Gyeongbuk, 39177 Korea
    nonzero@kumoh.ac.kr
  2. 2 Department of Computer Science, Chungbuk National University
    Chungdae-ro 1, Seowon-gu, Cheongju-si, Chungbuk, 28644 Korea
    kjcheoi@chungbuk.ac.kr

Abstract

In this paper, a hybrid visual attention model to effectively detect a distant target is proposed. The model employs the human visual attention mechanism and consists of two models, the training model, and the detection model. In the training model, some of the features are selected to train in the process of extracting and combining the early visual features from the training image of the target by bottom-up manner, and these features are trained and accumulated as trained data. When the image containing the target is input into the detection model, a task of selectively promoting only features of the target using pre-trained data is performed. As a result, the desired target is detected through the saliency map created as a result of the feature combination. The model has been tested on various images, and the experimental results demonstrate that the proposed model detected the target more accurately and faster than other previous models.

Key words

target, hybrid, saliency, attention

Digital Object Identifier (DOI)

https://doi.org/10.2298/CSIS200120001K

Publication information

Volume 18, Issue 2 (April 2021)
Special Issue on Emerging Services in the Next-Generation Web: Human Meets Artificial Intelligence
Year of Publication: 2021
ISSN: 1820-0214 (Print) 2406-1018 (Online)
Publisher: ComSIS Consortium

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How to cite

JaepilKo, Cheoi, K. J.: A Novel Distant Target Region Detection Method Using Hybrid Saliency-Based Attention Model Under Complex Textures. Computer Science and Information Systems, Vol. 18, No. 2, 379–399. (2021), https://doi.org/10.2298/CSIS200120001K