CNN Based Multi-Object Segmentation and Feature Fusion for Scene Recognition

作     者：Adnan Ahmed Rafique Yazeed Yasin Ghadi Suliman AAlsuhibany Samia Allaoua Chelloug Ahmad Jalal Jeongmin Park 

作者机构：Department of Computer ScienceAir UniversityIslamabad44000Pakistan Department of Computer Science and Software EngineeringAl Ain UniversityAl Ain15551UAE Department of Computer ScienceCollege of ComputerQassim UniversityBuraydah51452Saudi Arabia Department of Information TechnologyCollege of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer EngineeringKorea Polytechnic UniversitySiheung-siGyeonggi-do237Korea 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体(英文))

年 卷 期：2022年第73卷第12期

页      面：4657-4675页

核心收录：

学科分类：0711[理学-系统科学] 08[工学] 0835[工学-软件工程] 0701[理学-数学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：This research was supported by a grant(2021R1F1A1063634)of the Basic Science Research Program through the National Research Foundation(NRF)funded by the Ministry of Education Republic of Korea. 

主　　题：Convolutional neural network decision tree feature fusion neurofuzzy system 

摘      要：Latest advancements in vision technology offer an evident impact on multi-object recognition and scene understanding.Such sceneunderstanding task is a demanding part of several technologies,like augmented reality-based scene integration,robotic navigation,autonomous driving,and tourist guide.Incorporating visual information in contextually unified segments,convolution neural networks-based approaches will significantly mitigate the clutter,which is usual in classical frameworks during scene understanding.In this paper,we propose a convolutional neural network(CNN)based segmentation method for the recognition of multiple objects in an image.Initially,after acquisition and preprocessing,the image is segmented by using CNN.Then,CNN features are extracted from these segmented objects,and discrete cosine transform(DCT)and discrete wavelet transform(DWT)features are computed.After the extraction of CNN features and computation of classical machine learning features,fusion is performed using a fusion technique.Then,to select theminimal set of features,genetic algorithm-based feature selection is used.In order to recognize and understand the multi-objects in the scene,a neuro-fuzzy approach is applied.Once objects in the scene are recognized,the relationship between these objects is examined by employing the object-to-object relation approach.Finally,a decision tree is incorporated to assign the relevant labels to the scenes based on recognized objects in the image.The experimental results over complex scene datasets including SUN Red Green Blue-Depth(RGB-D)and Cityscapes’demonstrated a remarkable performance.