A Scene Images Classification Method Based on Local Binary Patterns and Nearest-Neighbor Classifier

Han, Guoqiang; Zhao, Chenglin

doi:10.1109/isda.2008.19

Cited by 6 publications

(2 citation statements)

References 12 publications

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“…The texture descriptor is obtained by concatenating the three resulting LBP histograms. This within component strategy was applied by several authors [ 34 , 35 , 36 , 37 , 38 ].…”

Section: Color Lbp Histogramsmentioning

confidence: 99%

Combination of LBP Bin and Histogram Selections for Color Texture Classification

et al. 2020

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LBP (Local Binary Pattern) is a very popular texture descriptor largely used in computer vision. In most applications, LBP histograms are exploited as texture features leading to a high dimensional feature space, especially for color texture classification problems. In the past few years, different solutions were proposed to reduce the dimension of the feature space based on the LBP histogram. Most of these approaches apply feature selection methods in order to find the most discriminative bins. Recently another strategy proposed selecting the most discriminant LBP histograms in their entirety. This paper tends to improve on these previous approaches, and presents a combination of LBP bin and histogram selections, where a histogram ranking method is applied before processing a bin selection procedure. The proposed approach is evaluated on five benchmark image databases and the obtained results show the effectiveness of the combination of LBP bin and histogram selections which outperforms the simple LBP bin and LBP histogram selection approaches when they are applied independently.

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“…The texture descriptor is obtained by concatenating the three resulting LBP histograms. This within component strategy was applied by several authors [ 34 , 35 , 36 , 37 , 38 ].…”

Section: Color Lbp Histogramsmentioning

confidence: 99%

Combination of LBP Bin and Histogram Selections for Color Texture Classification

et al. 2020

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“…A metric has been devised based on these features for categorizing and searching image databases. Han and Zhao (2008) have presented a visualization-based approach to train a texture classifier. Improved multi-channel LBP in RGB color space are used as textured color features and a K-NN is employed for visual training and classification; promising results have been reported in the classification of outdoor scene images.…”

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confidence: 99%

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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Expanding the Local Binary Pattern to Multispectral Images Using Total Orderings

Barra

2011

Communications in Computer and Information Science

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A Scene Images Classification Method Based on Local Binary Patterns and Nearest-Neighbor Classifier

Cited by 6 publications

References 12 publications

Combination of LBP Bin and Histogram Selections for Color Texture Classification

Combination of LBP Bin and Histogram Selections for Color Texture Classification

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

Expanding the Local Binary Pattern to Multispectral Images Using Total Orderings

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