Improved Local Ternary Patterns for Automatic Target Recognition in Infrared Imagery

Wu, Xiaofeng; Sun, Jianfang; Fan, Guoliang; Wang, Zhiheng

doi:10.3390/s150306399

Cited by 18 publications

(11 citation statements)

References 47 publications

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“…In the end, the LTP filter generates either one histogram of 3 P bins, or two histograms of 2 P bins that could be concatenated in a unique histogram of 2 P +1 bins. 9) Robust Local Ternary Patterns [62]: The RLTP filter is defined as a robust to noise version of the LTP filter. For each (P, R) neighborhood including the central pixel g c , the mean value µ c is retrieved.…”

Section: A Handcrafted Filters Of the Literaturementioning

confidence: 99%

“…10) Soft Concave-Convex Orthogonal Combination of Robust Local Ternary Patterns [62]: The SCCOCRLTP filter is based on the RLTP filter. It proposes to increase the number of discriminative patterns while reducing their memory footprint through the concepts of orthogonal combination [65] and concave-convex discrimination [55].…”

Section: A Handcrafted Filters Of the Literaturementioning

confidence: 99%

“…2) Light Combination of Local Binary Patterns: LCoLBP is a combination of LBP-like filters as done in the CLBP [23], SCCORLTP [62] and ELBP [37] filters. It is formally introduced in this paper.…”

Section: B Proposed Handcrafted Filtersmentioning

confidence: 99%

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Automatic Land Cover Reconstruction From Historical Aerial Images: An Evaluation of Features Extraction and Classification Algorithms

Ratajczak

Crispim-Junior

Faure

et al. 2019

IEEE Trans. on Image Process.

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The land cover reconstruction from monochromatic historical aerial images is a challenging task that has recently known an increasing interest from the scientific community with the proliferation of large scale epidemiological studies involving retrospective analysis of spatial pattern. However, the efforts engaged by the computer vision community in remote sensing applications are mostly focused on prospective approaches through the analysis of high resolution multi-spectral data acquired by advanced spatial programs. Hence, four contributions are proposed in this article. They aim at providing a comparison basis for the future development of computer vision algorithms applied to the automation of the land cover reconstruction from monochromatic historical aerial images. Firstly, a new multiscale multi-date dataset composed of 4.9 million non-overlapping annotated patches of the France territory between 1970 and 1990 has been created with the help of Geography experts. This dataset has been named HistAerial. Secondly, an extensive comparison study of state-of-the-art texture features extraction and classification algorithms including deep convolutional neural networks (DCNNs) has been performed. It is presented in the form of an evaluation. Thirdly, a novel low-dimensional local texture filter named Rotated-CorneR Local Binary Pattern (R-CRLBP) is presented as a simplification of the Binary Gradient Contours filter through the use of an orthogonal combination representation. Finally, a novel combination of low-dimensional texture descriptors, including the R-CRLBP filter, is introduced as a Light Combination of Local Binary Patterns (LCoLBP). The LCoLBP filter achieved state-of-the-art results on the HistAerial dataset while conserving a relatively low-dimensional feature vector space compared with the DCNN approaches (17 times shorter).

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Section: A Handcrafted Filters Of the Literaturementioning

confidence: 99%

Section: A Handcrafted Filters Of the Literaturementioning

confidence: 99%

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Automatic Land Cover Reconstruction From Historical Aerial Images: An Evaluation of Features Extraction and Classification Algorithms

Ratajczak

Crispim-Junior

Faure

et al. 2019

IEEE Trans. on Image Process.

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“…The CS-LBP based on central and symmetric idea can extract local texture feature. The texture features of face images do not change too much when the light, the posture, and the face expression change in face imagines [18][19][20]; therefore, this method using CS-LBP operator to extract face local texture features is effective. The CS-LBP is applied to compare ( =0, 1, 2, 3) with +4 ( and +4 are central symmetric pixels).…”

Section: Center-symmetric Local Binary Patterns (Cs-lbp)mentioning

confidence: 99%

The Face Recognition Method Based on CS-LBP and DBN

Sun

Yin

Yang

et al. 2018

Mathematical Problems in Engineering

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At present, the face recognition method based on deep belief network (DBN) has advantages of automatically learning the abstract information of face images and being affected slightly by active factors, so it becomes the main method in the face recognition area. Because DBN ignores the local information of face images, the face recognition rate based on DBN is badly affected. To solve this problem, a face recognition method based on center-symmetric local binary pattern (CS-LBP) and DBN (FRMCD) is proposed in this paper. Firstly, the face image is divided into several subblocks. Secondly, CS-LBP is used to extract texture features of each image subblock. Thirdly, texture feature histograms are formed and input into the DBN visual layer. Finally, face classification and face recognition are completed through deep learning in DBN. Through the experiments on face databases ORL, Extend Yale B, and CMU-PIE by the proposed method (FRMCD), the best partitioning way of the face image and the hidden unit number of the DBN hidden layer are obtained. Then, comparative experiments between the FRMCD and traditional methods are performed. The results show that the recognition rate of FRMCD is superior to those of traditional methods; the highest recognition rate is up to 98.82%. When the number of training samples is less, the FRMCD has more significant advantages. Compared with the method based on local binary pattern (LBP) and DBN, the time-consuming of FRMCD is shorter.

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“…Then this ternary pattern is converted into upper and lower binary bit patterns. Its improved versions known as Improved LTP (Wu, Sun, Fan, & Wang, 2015) gives better result. Noise-Resistant LBP (NR-LBP) (Ren, Jiang, & Yuan, 2013), Robust LBP(RLBP) (Zhao, Jia, Hu, & Min, 2013) Medical field is also benefitted by Content Based Image Retrieval in dealing with the large database since this helps in identifying diseases by retrieving similar images corresponding to a particular image.…”

Section: Related Workmentioning

confidence: 99%

Local Neighborhood Intensity Pattern–A new texture feature descriptor for image retrieval

Banerjee

Bhunia

Bhattacharyya

et al. 2018

Expert Systems with Applications

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In this paper, a new texture descriptor based on the local neighborhood intensity difference is proposed for content based image retrieval (CBIR). For computation of texture features like Local Binary Pattern (LBP), the center pixel in a 3×3 window of an image is compared with all the remaining neighbors, one pixel at a time to generate a binary bit pattern. It ignores the effect of the adjacent neighbors of a particular pixel for its binary encoding and also for texture description. The proposed method is based on the concept that neighbors of a particular pixel hold significant amount of texture information that can be considered for efficient texture representation for CBIR. The main impact of utilizing the mutual relationship among adjacent neighbors is that we do not rely on the sign of the intensity difference between central pixel and one of its neighbors ( ) only, rather we take into account the sign of difference values between and its adjacent neighbors along with the central pixels and same set of neighbors of . This makes our pattern more resistant to illumination changes. Moreover, most of the local patterns including LBP concentrates mainly on the sign information and thus ignores the magnitude. The magnitude information which plays an auxiliary role to supply complementary information of texture descriptor, is integrated in our approach by considering the mean of absolute deviation about each pixel from its adjacent neighbors. Taking this into account, we develop a new texture descriptor, named as Local Neighborhood Intensity Pattern (LNIP) which considers the relative intensity difference between a particular pixel and the center pixel by considering its adjacent neighbors and generate a sign and a magnitude pattern. Finally, the sign pattern and the magnitude pattern are concatenated into a single feature descriptor to generate a 2 more effective feature descriptor. The proposed descriptor has been tested for image retrieval on four databases, including three texture image databases -Brodatz texture image database, MIT VisTex database and Salzburg texture database and one face database -AT&T face database.The precision and recall values observed on these databases are compared with some state-of-art local patterns. The proposed method showed a significant improvement over many other existing methods.

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Improved Local Ternary Patterns for Automatic Target Recognition in Infrared Imagery

Cited by 18 publications

References 47 publications

Automatic Land Cover Reconstruction From Historical Aerial Images: An Evaluation of Features Extraction and Classification Algorithms

Automatic Land Cover Reconstruction From Historical Aerial Images: An Evaluation of Features Extraction and Classification Algorithms

The Face Recognition Method Based on CS-LBP and DBN

Local Neighborhood Intensity Pattern–A new texture feature descriptor for image retrieval

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