Hybrid Feature based Natural Scene Classification using Neural Network

Gupta, Deepika; Singh, Ajay Kumar; Kumari, Deepa; Raina, Raina

doi:10.5120/5629-7977

Cited by 4 publications

(1 citation statement)

References 16 publications

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“…It has been shown that the highest classification rate was obtained using the multi-scale wavelet-based features. A correct classification rate of 97% has been obtained which is comparable to similar neural networks classifiers used in [1], [2], [19]. The results are encouraging and promising.…”

Section: Sensitivity= True Positives/(true Positive + False Negative)supporting

confidence: 67%

Classification of Ultrasound Kidney Images using PCA and Neural Networks

Attia¹,

Abou-Chadi²,

Moustafa³

et al. 2015

ijacsa

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Section: Sensitivity= True Positives/(true Positive + False Negative)supporting

confidence: 67%

Classification of Ultrasound Kidney Images using PCA and Neural Networks

Attia¹,

Abou-Chadi²,

Moustafa³

et al. 2015

ijacsa

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Human-inspired features for natural scene classification

Ali

Fayek

Hemayed

2013

Pattern Recognition Letters

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Bridging the semantic gap with human perception based features for scene categorization

Shrivastava

Bhoyar

Zadgaonkar

2017

IJICC

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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.

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Hybrid Feature based Natural Scene Classification using Neural Network

Cited by 4 publications

References 16 publications

Classification of Ultrasound Kidney Images using PCA and Neural Networks

Classification of Ultrasound Kidney Images using PCA and Neural Networks

Human-inspired features for natural scene classification

Bridging the semantic gap with human perception based features for scene categorization

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