Research Article AData-DrivenandBiologicallyInspiredPreprocessingSchemeto Improve Visual Object Recognition Zahra Sadat Shariatmadar andKarimFaez Electrical Engineering Department, Amirkabir University of Technology, Tehran 15914, Iran Correspondence should be addressed to Karim Faez; kfaez@aut.ac.ir Received 14 October 2020; Revised 28 December 2020; Accepted 20 January 2021; Published 29 January 2021 Academic Editor: Akbar S. Namin Copyright © 2021 Zahra Sadat Shariatmadar and Karim Faez. is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Autonomous object recognition in images is one of the most critical topics in security and commercial applications. Due to recent advances in visual neuroscience, the researchers tend to extend biologically plausible schemes to improve the accuracy of object recognition. Preprocessing is one part of the visual recognition system that has received much less attention. In this paper, we propose a new, simple, and biologically inspired pre processing technique by using the data-driven mechanism of visual attention. In this part, the responses of Retinal Ganglion Cells (RGCs) are simulated. After obtaining these responses, an efficient threshold is selected. en, the points of the raw image with the most information are extracted according to it. en, the new images with these points are created, and finally, by combining these images with entropy coefficients, the most salient object is located. After extracting appropriate features, the classifier categorizes the initial image into one of the predefined object categories. Our system was evaluated on the Caltech-101 dataset. Experimental results demonstrate the efficacy and effectiveness of this novel method of preprocessing. 1.Introduction One of the challenges in the field of artificial intelligence is object recognition. e objective of this process is to classify an object into one of the predefined categories. ere are various challenges in this field, such as cluttered and noisy background or objects under different illumi- nation and contrast environments. Human beings can detectandclassifyobjectswithoutanyeffortinashorttime. Researchers believe that the recognition system is closer to the human visual system will be better. In other words, numerous studies [1–3] have shown that inspired by the human visual system, the recognition system can be designed with relatively high accuracy. According to the recent advances in visual neuroscience, the researchers tend to develop biologically plausible algorithms to im- prove the accuracy of the object recognition system. Object recognition considerably relies on image representation, for which, in this paper, a novel biologically inspired model is presented for this stage. Among image representation models, bag-of-words (BoW) representation [4] has been generally employed because it is robust to object scale and translation changes. ree different modules of BoW models are extraction, coding, and pooling of different features. K-means clustering, which is applied for feature coding, will cause severe information loss because of the hard assignment of each feature to the nearest cluster center. So, soft k-means [5] and sparse coding [6] proce- dures are presented to overcome this problem. Sparse coding-based methods are broadly used since they have fewer parameters and more reliable performance than soft k-means. Some different sparse coding-related feature coding techniques [6–8] are offered and obtain the best achievement for image presentation. During the sparse coding-based strategies, the image is represented by a vector of sparse codes matching the features in the individual image within the feature pooling module. In the BoW model, the whole image is the pooling area, and therefore, the spatial information may be lost. Such data can significantly affect recognition accuracy. Hindawi Computational Intelligence and Neuroscience Volume 2021, Article ID 6699335, 13 pages https://doi.org/10.1155/2021/6699335