These last few years, several supervised scores have been proposed in the literature to select histograms. Applied to color texture classification problems, these scores have improved the accuracy by selecting the mos...
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These last few years, several supervised scores have been proposed in the literature to select histograms. Applied to color texture classification problems, these scores have improved the accuracy by selecting the most discriminant histograms among a set of available ones computed from a color image. In this paper, two new scores are proposed to select histograms: The adapted Variance score and the adapted Laplacian score. These new scores are computed without considering the class label of the images, contrary to what is done until now. Experiments, achieved on OuTex, USPTex, and BarkTex sets, show that these unsupervised scores give as good results as the supervised ones for LBP histogram selection.
We propose a novel intuitionistic fuzzy feature extraction method to encode local texture. The proposed method extends the fuzzy local binary pattern approach by incorporating intuitionistic fuzzy set theory in the re...
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The combination texture feature extraction approach for texture image retrieval is proposed in this paper. Two kinds of low level texture features were combined in the approach. One of them was extracted from singular...
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The combination texture feature extraction approach for texture image retrieval is proposed in this paper. Two kinds of low level texture features were combined in the approach. One of them was extracted from singular value decomposition (SVD) based dual-tree complex wavelet transform (DTCWT) coefficients, and the other one was extracted from multi-scale local binary patterns (LBPs). The fusion features of SVD based multi-directional wavelet features and multi-scale LBP features have short dimensions of feature vector. The comparing experiments are conducted on Brodatz and Vistex datasets. According to the experimental results, the proposed method has a relatively better performance in aspect of retrieval accuracy and time complexity upon the existing methods.
Facial expression recognition (FER) is an important task for various computer vision applications. The task becomes challenging when it requires the detection and encoding of macro-and micropatterns of facial expressi...
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Facial expression recognition (FER) is an important task for various computer vision applications. The task becomes challenging when it requires the detection and encoding of macro-and micropatterns of facial expressions. We present a two-stage texture feature extraction framework based on the local binary pattern (LBP) variants and evaluate its significance in recognizing posed and nonposed facial expressions. We focus on the parametric limitations of the LBP variants and investigate their effects for optimal FER. The size of the local neighborhood is an important parameter of the LBP technique for its extraction in images. To make the LBP adaptive, we exploit the granulometric information of the facial images to find the local neighborhood size for the extraction of center-symmetric LBP (CS-LBP) features. Our two-stage texture representations consist of an LBP variant and the adaptive CS-LBP features. Among the presented two-stage texture feature extractions, the binarized statistical image features and adaptive CS-LBP features were found showing high FER rates. Evaluation of the adaptive texture features shows competitive and higher performance than the nonadaptive features and other state-of-the-art approaches, respectively. (C) 2017 SPIE and IS&T
Early diagnosis of breast cancer can improve the survival rate by detecting cancer at initial stage. In this paper, an efficient computer-based mammogram retrieval system is proposed, which helps in early diagnosis of...
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ISBN:
(纸本)9781509062386
Early diagnosis of breast cancer can improve the survival rate by detecting cancer at initial stage. In this paper, an efficient computer-based mammogram retrieval system is proposed, which helps in early diagnosis of breast cancer by comparing the current case with past cases. The proposed steps include cropping of mammograms, feature extraction using local binary pattern (LBP) and k-mean clustering. Using LBP, k-mean generates the clusters based on the visual similarity of mammograms. Further, query image features are matched with all cluster representatives to find the closest cluster. Finally, images are retrieved from this closest cluster using Euclidean distance similarity measure. So, at the searching time the query image is searched only in small subset depending upon cluster size and is not compared with all the images in the database, reflects a superior response time with good retrieval performances. Experiments on benchmark mammography image analysis society (MIAS) database confirm the effectiveness of this work.
The volume of digitised documents is increasing every day. Thus, designing a fast document image retrieval method for the large volume of document images, especially when the document images are also large in size, is...
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ISBN:
(纸本)9781538642764
The volume of digitised documents is increasing every day. Thus, designing a fast document image retrieval method for the large volume of document images, especially when the document images are also large in size, is of high demand. As feature extraction is one of the important steps in every document image retrieval system, a feature extraction technique with a low computing time and small feature number has a direct effect on the speed of the retrieval system. In this paper, we propose a non-parametric texture feature extraction method based on summarising the local grey-level structure of the image. To extract the proposed features, the input image is, at first, divided into a set of overlapping patches of equal size. The peripheral pixels of the centre pixel in a patch are used to extract two sets of patterns. The patterns are derived from the vertical & horizontal, and diagonal & off-diagonal pixels of the patch, separately. From each set of pixels, 15 different local binary patterns are extracted in our proposed feature extraction method. Two histograms of the local binary patterns are then created and concatenated to obtain 30 features called fast local binary pattern (F-LBP). To evaluate the efficiency of the proposed feature extraction method, MTDB and ITESOFT databases were considered for experimentation. The proposed FLBP provided promising results with lower computing time as well as smaller memory space consumption compared to other variation of LBP methods.
Malware classification is a critical part in the cyber-security. Traditional methodologies for the malware classification typically use static analysis and dynamic analysis to identify malware. In this paper, a malwar...
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ISBN:
(纸本)9781538627150
Malware classification is a critical part in the cyber-security. Traditional methodologies for the malware classification typically use static analysis and dynamic analysis to identify malware. In this paper, a malware classification methodology based on its binary image and extracting local binary pattern (LBP) features is proposed. First, malware images are reorganized into 3 by 3 grids which is mainly used to extract LBP feature. Second, the LBP is implemented on the malware images to extract features in that it is useful in pattern or texture classification. Finally, Tensorflow, a library for machine learning, is applied to classify malware images with the LBP feature. Performance comparison results among different classifiers with different image descriptors such as GIST, a spatial envelop, and the LBP demonstrate that our proposed approach outperforms others.
A compromised biometric is compromised forever and the user is no more secure in other database also. This challenge can be addressed by Cancelable Biometric. A Cancelable Biometric is distorted version of a original ...
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ISBN:
(纸本)9781538642832
A compromised biometric is compromised forever and the user is no more secure in other database also. This challenge can be addressed by Cancelable Biometric. A Cancelable Biometric is distorted version of a original biometric, that can be canceled and reissued like a password, and also is unique for every application. Cancelable biometric for original biometric has been seriously understudied problem. This paper presents a novel Cancelable coding scheme based on local binary pattern (LBP) and Random Projection, where the biometric features are distorted in a revocable but irreversible manner by first transforming the raw biometric data into a fixed-length feature vector and then projecting it onto randomly selected subspace using set of random numbers. Three methods has been presented and experimented thoroughly. The Cancelable palmprint with local Ternary pattern (LTP) and two random number set outperform the other two methods. The proposed scheme has been verified under the best case and worse case scenarios (normal and stolen token scenario) on the PolyU Database.
Sound event classification is a basic approach for real-world application. This paper presents feature extraction technique for classifying audio data. The audios are classified by combining of digital signal and digi...
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ISBN:
(纸本)9781509067305
Sound event classification is a basic approach for real-world application. This paper presents feature extraction technique for classifying audio data. The audios are classified by combining of digital signal and digital image processing. Firstly, the audio data is partitioned into fixed length and each portion is transformed into spectrogram image. The, the distinct features are extracted from this spectrogram using bidirectional local binary pattern. Finally, support vector machine and k-NN are used for classification. The method is tested on an audio database of ESC-10 sound event data.
Fragment reconstruction aims to restore broken images and documents via matching spatial adjacent fragments. As the existing solutions in the literature still remain problematic, we present a novel feature descriptor,...
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ISBN:
(纸本)9781509060672
Fragment reconstruction aims to restore broken images and documents via matching spatial adjacent fragments. As the existing solutions in the literature still remain problematic, we present a novel feature descriptor, Normal Direction local binary pattern (termed as ND-LBP), for document/image fragment matching. ND-LBP is based on the conventional LBP descriptor, however, it outstands LBP by introducing new features derived from shapes and contents of fragments to promote its discrimination. With normal direction operation, ND-LBP is rotation-invariant, and thus could effectively and efficiently match fragments of arbitrary orientation. According to our extensive evaluations on real world datasets, the fragment reconstruction approach with ND-LBP feature has high precision, robustness and efficiency, and outperforms existing features.
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