Fuzzy Based Texton Binary Shape Matrix (FTBSM) for Texture Classification

Texton is a extensively applied approach for texture analysis. This technique shows a strong dependence on certain number of parameters. Unfortunately, each variation of values of any parameter may affect the texture characterization performance. Moreover, micro structure texton is unable to extract texture features which also have a negative effect on the classification task. This paper, deals with a new descriptor which avoids the drawbacks mentioned above. To address the above, the present paper derives a new descriptor called Fuzzy Based Texton Binary Shape Matrix (FTBSM) for clear variation of any feature/parameter. The proposed FTBSM are defined based on similarity of neighboring edges on a 3D7;3 neighborhood. With micro-structures serving as a bridge for extracting shape features and it effectively integrates color, texture and shape component information as a whole for texture classification. The proposed FTBSM algorithm exhibits low dimensionality. The proposed FTBSM method is tested on Vistex and Akarmarble texture datasets of natural images. The results demonstrate that it is much more efficient and effective than representative feature descriptors, such as logical operators and GLCM and LBP, for texture classification

Texture Analysis and Classification Based on Fuzzy Triangular Greylevel Pattern and Run-Length Features

Your Texture analysis is one of the most important techniques used in the analysis and interpretation of images, consisting of repetition or quasi repetition of some fundamental image elements. The present paper derived Fuzzy Triangular Greylevel Pattern (FTGP) to overcome the disadvantages of LBP and other local approaches. The FTGP is a 2 x 2 matrix that is derived from a 3 x 3 neighborhood matrix. The proposed FTGP scheme reduces the overall dimension of the image while preserving the significant attributes, primitives, and properties of the local texture. From each 3 x 3 matrix a Local Grey level Matrix (LGM) is formed by subtracting local neighborhoods by the gray value of its center. The 2 x 2 FTGP is generated from LGM by taking the average value of the Triangular Neighbor Pixels (TNP) of the 3 x 3 LGM. A fuzzy logic is applied to convert the Triangular Neighborhood Matrix (TNM) in to fuzzy patterns with 5 values {0, 1, 2, 3 and 4} instead of patterns of LBP which has two values {0, 1}. On these fuzzy patterns a set of Run Length features are evaluated for an efficient classification. The proposed method is experimented with wide variety of textures, and exhibited with a high classification rate. The proposed FTGP with run length features shown its supremacy and efficacy over the various existing methods in classification of textures

Effects of cultural characteristics on building an emotion classifier through facial expression analysis

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Facial expressions are an important demonstration of humanity's humors and emotions. Algorithms capable of recognizing facial expressions and associating them with emotions were developed and employed to compare the expressions that different cultural groups use to show their emotions. Static pictures of predominantly occidental and oriental subjects from public datasets were used to train machine learning algorithms, whereas local binary patterns, histogram of oriented gradients (HOGs), and Gabor filters were employed to describe the facial expressions for six different basic emotions. The most consistent combination, formed by the association of HOG filter and support vector machines, was then used to classify the other cultural group: there was a strong drop in accuracy, meaning that the subtle differences of facial expressions of each culture affected the classifier performance. Finally, a classifier was trained with images from both occidental and oriental subjects and its accuracy was higher on multicultural data, evidencing the need of a multicultural training set to build an efficient classifier. (C) 2015 SPIE and IS&TFacial expressions are an important demonstration of humanity's humors and emotions. Algorithms capable of recognizing facial expressions and associating them with emotions were developed and employed to compare the expressions that different cultural gro24219FAPESP - FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULOCNPQ - CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICOFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)FAPESP [2011/22749-8, 2014/04020-9]CNPq [307113/2012-4]2011/22749-8; 2014/04020-9307113/2012-

A CNN Based Approach for Garments Texture Design Classification

Identifying garments texture design automatically for recommending the fashion trends is important nowadays because of the rapid growth of online shopping. By learning the properties of images efficiently, a machine can give better accuracy of classification. Several Hand-Engineered feature coding exists for identifying garments design classes. Recently, Deep Convolutional Neural Networks (CNNs) have shown better performances for different object recognition. Deep CNN uses multiple levels of representation and abstraction that helps a machine to understand the types of data more accurately. In this paper, a CNN model for identifying garments design classes has been proposed. Experimental results on two different datasets show better results than existing two well-known CNN models (AlexNet and VGGNet) and some state-of-the-art Hand-Engineered feature extraction methods

Innovative local texture descriptors with application to eye detection

Local Binary Patterns (LBP), which is one of the well-known texture descriptors, has broad applications in pattern recognition and computer vision. The attractive properties of LBP are its tolerance to illumination variations and its computational simplicity. However, LBP only compares a pixel with those in its own neighborhood and encodes little information about the relationship of the local texture with the features. This dissertation introduces a new Feature Local Binary Patterns (FLBP) texture descriptor that can compare a pixel with those in its own neighborhood as well as in other neighborhoods and encodes the information of both local texture and features. The features encoded in FLBP are broadly defined, such as edges, Gabor wavelet features, and color features. Specifically, a binary image is first derived by extracting feature pixels from a given image, and then a distance vector field is obtained by computing the distance vector between each pixel and its nearest feature pixel defined in the binary image. Based on the distance vector field and the FLBP parameters, the FLBP representation of the given image is derived. The feasibility of the proposed FLBP is demonstrated on eye detection using the BioID and the FERET databases. Experimental results show that the FLBP method significantly improves upon the LBP method in terms of both the eye detection rate and the eye center localization accuracy. As LBP is sensitive to noise especially in near-uniform image regions, Local Ternary Patterns (LTP) was proposed to address this problem by extending LBP to three-valued codes. However, further research reveals that both LTP and LBP achieve similar results for face and facial expression recognition, while LTP has a higher computational cost than LBP. To improve upon LTP, this dissertation introduces another new local texture descriptor: Local Quaternary Patterns (LQP) and its extension, Feature Local Quaternary Patterns (FLQP). LQP encodes four relationships of local texture, and therefore, it includes more information of local texture than the LBP and the LTP. FLQP, which encodes both local and feature information, is expected to perform even better than LQP for texture description and pattern analysis. The LQP and FLQP are applied to eye detection on the BioID database. Experimental results show that both FLQP and LQP achieve better eye detection performance than FLTP, LTP, FLBP and LBP. The FLQP method achieves the highest eye detection rate

Towards Realistic Facial Expression Recognition

Automatic facial expression recognition has attracted significant attention over the past decades. Although substantial progress has been achieved for certain scenarios (such as frontal faces in strictly controlled laboratory settings), accurate recognition of facial expression in realistic environments remains unsolved for the most part. The main objective of this thesis is to investigate facial expression recognition in unconstrained environments. As one major problem faced by the literature is the lack of realistic training and testing data, this thesis presents a web search based framework to collect realistic facial expression dataset from the Web. By adopting an active learning based method to remove noisy images from text based image search results, the proposed approach minimizes the human efforts during the dataset construction and maximizes the scalability for future research. Various novel facial expression features are then proposed to address the challenges imposed by the newly collected dataset. Finally, a spectral embedding based feature fusion framework is presented to combine the proposed facial expression features to form a more descriptive representation. This thesis also systematically investigates how the number of frames of a facial expression sequence can affect the performance of facial expression recognition algorithms, since facial expression sequences may be captured under different frame rates in realistic scenarios. A facial expression keyframe selection method is proposed based on keypoint based frame representation. Comprehensive experiments have been performed to demonstrate the effectiveness of the presented methods