Subclass Discriminant Analysis of Morphological and Textural Features for HEp-2 Staining Pattern Classification

Classifying HEp-2 fluorescence patterns in Indirect Immunofluorescence (IIF) HEp-2 cell imaging is important for the differential diagnosis of autoimmune diseases. The current technique, based on human visual inspection, is time-consuming, subjective and dependent on the operator's experience. Automating this process may be a solution to these limitations, making IIF faster and more reliable. This work proposes a classification approach based on Subclass Discriminant Analysis (SDA), a dimensionality reduction technique that provides an effective representation of the cells in the feature space, suitably coping with the high within-class variance typical of HEp-2 cell patterns. In order to generate an adequate characterization of the fluorescence patterns, we investigate the individual and combined contributions of several image attributes, showing that the integration of morphological, global and local textural features is the most suited for this purpose. The proposed approach provides an accuracy of the staining pattern classification of about 90%

Feature Fusion for Pattern Recognition

Information or data fusion is one of the solutions adopted for improving the performance of a pattern recognition system. Information can be gathered either from multiple data sources or through the use of multiple representations generated from a single data source. A single representation summarizes the information and provides a single cue on the data, and thus may not be able to fully reveal the inherent characteristics of the data. In visual recognition, image representations are generally categorized into global and local based types. A global representation captures features corresponds to some holistic characteristic in the image, and produces a coarse representation. Differently, a local representation reveals detail variations and traits inherent to the image. Psychological findings have shown that humans equally rely on both local and global visual information. Moreover, there is a large agreement in literature that the combination of different features, i.e. a multiview perspective, can have a positive effect on the performance of a pattern recognition system. In fact, different features can represent different and complementary characteristics of the data; in other words, each feature set represent a different view on the original dataset. Thus it is expected that a visual recognition system can benefit from different representations (both local and global) through the use of information fusion. Information can generally be consolidated at three different levels: (i) decision level; (ii) match score level; and (iii) feature level. In the literature match level and decision level fusion (i.e. combining the output of different classifier, each of them working on different feature sets) have been extensively studied, whereas feature level fusion is a relatively understudied problem because of the difficulties inherent to its correct implementation. Feature level fusion may incorporate redundant, noisy or trivial information and the concatenated feature vectors may lead to the problem of curse of dimensionality. In addition, the feature sets may not be compatible and relationship between different feature spaces may not be known. Moreover, this integration comes at a cost, which may incur in units of time, computational resources or even money. Nevertheless, it is thought that fusing features at this level would still retain a richer source of discriminative information. Motivated by the belief, this thesis investigates the use of feature level fusion and its correlation with feature selection and classification tasks for two recent pattern recognition problems. These include the classification of six types of HEp2 staining patterns and the automatic verification of kinship relations in a pair of face images. several image attributes are proposed, that are better capable of characterizing the different kind of images associated with the two said classification tasks. Feature level fusion of the different attributes is performed followed by a careful reduction of features, through the use of pertinent feature selection and classification algorithms, that decide the most representative and discriminative feature sets for the patterns to classify. Results indicate that the proposed techniques working on the combination of features of different natures, which are capable of describing the data under different perspectives, is an effective strategy in achieving higher accurac

Feature Fusion for Pattern Recognition

Information or data fusion is one of the solutions adopted for improving the performance of a pattern recognition system. Information can be gathered either from multiple data sources or through the use of multiple representations generated from a single data source. A single representation summarizes the information and provides a single cue on the data, and thus may not be able to fully reveal the inherent characteristics of the data. In visual recognition, image representations are generally categorized into global and local based types. A global representation captures features corresponds to some holistic characteristic in the image, and produces a coarse representation. Differently, a local representation reveals detail variations and traits inherent to the image. Psychological findings have shown that humans equally rely on both local and global visual information. Moreover, there is a large agreement in literature that the combination of different features, i.e. a multiview perspective, can have a positive effect on the performance of a pattern recognition system. In fact, different features can represent different and complementary characteristics of the data; in other words, each feature set represent a different view on the original dataset. Thus it is expected that a visual recognition system can benefit from different representations (both local and global) through the use of information fusion. Information can generally be consolidated at three different levels: (i) decision level; (ii) match score level; and (iii) feature level. In the literature match level and decision level fusion (i.e. combining the output of different classifier, each of them working on different feature sets) have been extensively studied, whereas feature level fusion is a relatively understudied problem because of the difficulties inherent to its correct implementation. Feature level fusion may incorporate redundant, noisy or trivial information and the concatenated feature vectors may lead to the problem of curse of dimensionality. In addition, the feature sets may not be compatible and relationship between different feature spaces may not be known. Moreover, this integration comes at a cost, which may incur in units of time, computational resources or even money. Nevertheless, it is thought that fusing features at this level would still retain a richer source of discriminative information. Motivated by the belief, this thesis investigates the use of feature level fusion and its correlation with feature selection and classification tasks for two recent pattern recognition problems. These include the classification of six types of HEp2 staining patterns and the automatic verification of kinship relations in a pair of face images. several image attributes are proposed, that are better capable of characterizing the different kind of images associated with the two said classification tasks. Feature level fusion of the different attributes is performed followed by a careful reduction of features, through the use of pertinent feature selection and classification algorithms, that decide the most representative and discriminative feature sets for the patterns to classify. Results indicate that the proposed techniques working on the combination of features of different natures, which are capable of describing the data under different perspectives, is an effective strategy in achieving higher accuracy

A Multi-perspective Holistic Approach to Kinship Verification in the Wild

The automatic verification of kinship is a challenging problem that has recently attracted much interest from the research community. It consists in telling whether two individuals are related or not, based on the analysis of their facial images. This is a challenging task since it has to deal with differences in race, gender and age between subjects. In addition, the unpredictable amount of genetic information shared by relatives reflects into individuals showing different degrees of facial similarity. Kinship recognition in the wild introduces more difficulties, since the images to be analyzed can have low resolutions, different illuminations, resolutions, face orientations, expressions and occlusions. Due to the characteristics of the image in analysis, which highly reduces the discriminative power of local features, we address kinship recognition in the wild with a multi-perspective holistic approach. The image pairs to be labeled as kin or non-kin are first characterized by selecting the most relevant variables from the combination of different global textural features. The resulting feature vectors are then used to feed an SVM classifier, which has been assessed on the Kinship Face in the Wild dataset over different sub-classes of parent-child relationships. Results of our experiments show that our method provides optimal accuracies with respect to other approaches on the same data and outperforms the recognition abilities of human beings

Robust multi-scale orientation estimation: Directional filter bank based approach

Orientation estimation is considered as an important task in many subsequent pattern recognition and image enhancement systems. In a noisy environment, the gradient-based estimator provides poor results. A pre-smoothing Gaussian function with an appropriate scale is conventionally used to get improved gradients. Later on, a family of pre-smoothing Gaussian functions with a range of scales is employed for estimation, this is referred to as multi-scale orientation estimator. To provide groundwork for comparison, a more formal framework of multi-scale orientation estimation, based on scale-space axioms, in spatial domain is presented. Then for improvement purposes a Fourier domain approach, where directional filter bank (DFB) structure is embedded in multi-scale orientation estimation framework, is proposed. This is referred to as multi-scale DFB approach. The paper presents the comparison work for estimation of local orientations using multi-scale approaches both in spatial and Fourier domain. In the Fourier-domain approach, two linear combinations are deployed, one across the directional image, and the other across the scales. This is opposed to only one linear combination across the scales, used in simple spatial domain techniques. Further more, the DFB-based Fourier domain approach extracts the best local orientation by comparing and contrasting all possible orientations with their respective strength measures. The strength measure used in Fourier method is based on local variance, free from inaccurate gradient calculation. Simulations are conducted over noisy test images as well as real fingerprints. Our objective results indicate that multi-scale Fourier domain approach always yields better estimates at variable level of noise as compared to stand alone multi-scale spatial domain approaches. The improvements made by Fourier domain estimate can largely be attributed to the use of double linear combination both across the directional bands and across the scales

The FG 2015 Kinship Verification in the Wild Evaluation

International audienceThe aim of the Kinship Verification in the Wild Evaluation (held in conjunction with the 2015 IEEE International Conference on Automatic Face and Gesture Recognition, Ljubljana, Slovenia) was to evaluate different kinship verification algorithms. For this task, two datasets were made available and three possible experimental protocols (unsupervised, image-restricted, and image-unrestricted) were designed. Five institutions submitted their results to the evaluation: (i) Politecnico di Torino, Italy; (ii) LIRIS-University of Lyon, France; (iii) Universidad de Las Palmas de Gran Canaria, Spain; (iv) Nanjing University of Aeronautics and Astronautics, China; and (v) Bar Ilan University, Israel. Most of the participants tackled the image-restricted challenge and experimental results demonstrated better kinship verification performance than the baseline methods provided by the organizers