Purpose–The purpose of this paper is to build a classification system which mimics the perceptual ability of human vision,in gathering knowledge about the structure,content and the surrounding environment of a real-w...Purpose–The purpose of this paper is to build a classification system which mimics the perceptual ability of human vision,in gathering knowledge about the structure,content and the surrounding environment of a real-world natural scene,at a quick glance accurately.This paper proposes a set of novel features to determine the gist of a given scene based on dominant color,dominant direction,openness and roughness features.Design/methodology/approach–The classification system is designed at two different levels.At the first level,a set of low level features are extracted for each semantic feature.At the second level the extracted features are subjected to the process of feature evaluation,based on inter-class and intra-class distances.The most discriminating features are retained and used for training the support vector machine(SVM)classifier for two different data sets.Findings–Accuracy of the proposed system has been evaluated on two data sets:the well-known Oliva-Torralba data set and the customized image data set comprising of high-resolution images of natural landscapes.The experimentation on these two data sets with the proposed novel feature set and SVM classifier has provided 92.68 percent average classification accuracy,using ten-fold cross validation approach.The set of proposed features efficiently represent visual information and are therefore capable of narrowing the semantic gap between low-level image representation and high-level human perception.Originality/value–The method presented in this paper represents a new approach for extracting low-level features of reduced dimensionality that is able to model human perception for the task of scene classification.The methods of mapping primitive features to high-level features are intuitive to the user and are capable of reducing the semantic gap.The proposed feature evaluation technique is general and can be applied across any domain.展开更多
文摘Purpose–The purpose of this paper is to build a classification system which mimics the perceptual ability of human vision,in gathering knowledge about the structure,content and the surrounding environment of a real-world natural scene,at a quick glance accurately.This paper proposes a set of novel features to determine the gist of a given scene based on dominant color,dominant direction,openness and roughness features.Design/methodology/approach–The classification system is designed at two different levels.At the first level,a set of low level features are extracted for each semantic feature.At the second level the extracted features are subjected to the process of feature evaluation,based on inter-class and intra-class distances.The most discriminating features are retained and used for training the support vector machine(SVM)classifier for two different data sets.Findings–Accuracy of the proposed system has been evaluated on two data sets:the well-known Oliva-Torralba data set and the customized image data set comprising of high-resolution images of natural landscapes.The experimentation on these two data sets with the proposed novel feature set and SVM classifier has provided 92.68 percent average classification accuracy,using ten-fold cross validation approach.The set of proposed features efficiently represent visual information and are therefore capable of narrowing the semantic gap between low-level image representation and high-level human perception.Originality/value–The method presented in this paper represents a new approach for extracting low-level features of reduced dimensionality that is able to model human perception for the task of scene classification.The methods of mapping primitive features to high-level features are intuitive to the user and are capable of reducing the semantic gap.The proposed feature evaluation technique is general and can be applied across any domain.
