Multiresolution gray-scale and rotation invariant texture classification with local binary patterns

Ojala, Timo; Pietikäinen, Matti; Mäenpää, Topi

doi:10.1109/tpami.2002.1017623

Cited by 13,527 publications

(8,021 citation statements)

References 32 publications

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“…Initially frontal view image is segmented into three regions; upper region E(eye and eyebrow), middle region N(Nose) and lower region M(mouth). The uniform rotation-invariant LBP (RIULBP [18]) texture feature distributions are extracted from these three regions and represented as a weighted 7 dimensional histogram descriptor .…”

Section: Methodsologymentioning

confidence: 99%

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A Neuro Fuzzy Approach for Facial Expression Recognition Using LBP Histograms

Gomathi¹,

Ramar²,

Jeevakumar³

2010

IJCTE

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Section: Methodsologymentioning

confidence: 99%

“…Ojala et al [18], found that the vast majority of the LBP patterns in a local neighborhood are so called "uniform patterns". An LBP is called uniform if the binary pattern contains at most two bitwise transitions from 0 to 1 or vice versa when it is considered circular.…”

Section: A Local Binary Patterns (Lbp)mentioning

confidence: 99%

A Neuro Fuzzy Approach for Facial Expression Recognition Using LBP Histograms

Gomathi¹,

Ramar²,

Jeevakumar³

2010

IJCTE

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“…Ojala et al [30] proposed multiresolution approach to grayscale and rotation invariant texture classification based on LBP. They presented a uniformity measure U, which corresponds to the number of spatial transitions (bitwise 0/1 changes) in the pattern as follows:…”

Section: Texture Featuresmentioning

confidence: 99%

Optimal Query-Based Relevance Feedback in Medical Image Retrieval Using Score Fusion-Based Classification

Behnam

Pourghassem

2014

J Digit Imaging

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In this paper, a new content-based medical image retrieval (CBMIR) framework using an effective classification method and a novel relevance feedback (RF) approach are proposed. For a large-scale database with diverse collection of different modalities, query image classification is inevitable due to firstly, reducing the computational complexity and secondly, increasing influence of data fusion by removing unimportant data and focus on the more valuable information. Hence, we find probability distribution of classes in the database using Gaussian mixture model (GMM) for each feature descriptor and then using the fusion of obtained scores from the dependency probabilities, the most relevant clusters are identified for a given query. Afterwards, visual similarity of query image and images in relevant clusters are calculated. This method is performed separately on all feature descriptors, and then the results are fused together using feature similarity ranking level fusion algorithm. In the RF level, we propose a new approach to find the optimal queries based on relevant images. The main idea is based on density function estimation of positive images and strategy of moving toward the aggregation of estimated density function. The proposed framework has been evaluated on ImageCLEF 2005 database consisting of 10,000 medical X-ray images of 57 semantic classes. The experimental results show that compared with the existing CBMIR systems, our framework obtains the acceptable performance both in the image classification and in the image retrieval by RF.

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“…It was rotated-invariant system and here feature vector length was not lengthy. LBP is justified to be an impressive texture descriptor and practiced for pattern recognition research work e.g., facial expression recognition [23], [24]. Both local binary pattern and local phase quantization method were applied but system is time consuming for a facial expression recognition system in [25].…”

Section: Introductionmentioning

confidence: 99%

Wavelet-threshold based Bit Intensity Measurement: On Facial Expression Recognition

Ane¹

2016

IJCA

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The objective of this paper is to examine the threshold concept for facial expression recognition, in facial image which is mostly preferred to represent facial expressions in computer vision applications, including face recognition and expression analysis. The goal of this study, bit intensity measurement with thresholding method is applied on facial image for expression recognition. Each image partitioned into equal regions from which the feature binary attributes are extracted and integrated into a feature vector to be used as a face descriptor. Binary measurement has been done matching with reference and neighbouring pixels. Extracted local binary features are integrated for sorting into thresholding based similarity index according to their wave-threshold coefficient values. For evaluation the proposed descriptors JAFFE dataset, Support Vector Machine, Cross validation, fdlibmex were applied.

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Multiresolution gray-scale and rotation invariant texture classification with local binary patterns

Cited by 13,527 publications

References 32 publications

A Neuro Fuzzy Approach for Facial Expression Recognition Using LBP Histograms

A Neuro Fuzzy Approach for Facial Expression Recognition Using LBP Histograms

Optimal Query-Based Relevance Feedback in Medical Image Retrieval Using Score Fusion-Based Classification

Wavelet-threshold based Bit Intensity Measurement: On Facial Expression Recognition

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