Image Semantic Recognition and Segmentation Algorithm of Colorimetric Sensor Array Based on Deep Convolutional Neural Network. (30th September 2022)

• Record Type: Journal Article
• Authors: Tang, Jingjing; Wang, Li; Huang, Jing; Shi, Aiye; Xu, Lizhong
• Other Names: Ye Jun Academic Editor.
• Is Part Of: Computational intelligence and neuroscience. Volume 2022(2022)
• Publication Date: 2022-09-30
• Subjects: Neurosciences -- Data processing -- Periodicals; Computational intelligence -- Periodicals; Computational neuroscience -- Periodicals; 612.80285
• DOI: 10.1155/2022/2439371
• Languages: English
• ISSNs: 1687-5265

Abstract

Abstract : Semantic feature recognition in colour images is required for identifying uneven patterns in object detection and classification. The semantic features are identified by segmenting the colorimetric sensor array features through machine learning paradigms. Semantic segmentation is a method for identifying distinct elements in an image. This can be considered a task involving image classification at the pixel level. This article introduces a semantic feature-dependent array segmentation method (SFASM) to improve recognition accuracy due to irregular semantics. The proposed method incorporates a deep convolutional neural network for detecting the semantic and un-semantic features based on sensor array representations. The colour distributions per array are identified for horizontal and vertical semantics analysis. In this analysis, deep learning classifies the uneven patterns based on colour distribution, i.e. the consecutive and scattered colour distribution pixels in an array are correlated for their similarity. This similarity identification is maximized through max-pooling and recurrent iterations, preventing detection errors. The proposed method classifies the semantic features for further correlation sections, improving the accuracy. The proposed method's performance is thus validated using the metrics precision, analysis time and F 1-Score.