Toward a General-Purpose Heterogeneous Ensemble for Pattern Classification

We perform an extensive study of the performance of different classification approaches on twenty-five datasets (fourteen image datasets and eleven UCI data mining datasets). The aim is to find General-Purpose (GP) heterogeneous ensembles (requiring little to no parameter tuning) that perform competitively across multiple datasets. The state-of-the-art classifiers examined in this study include the support vector machine, Gaussian process classifiers, random subspace of adaboost, random subspace of rotation boosting, and deep learning classifiers. We demonstrate that a heterogeneous ensemble based on the simple fusion by sum rule of different classifiers performs consistently well across all twenty-five datasets. The most important result of our investigation is demonstrating that some very recent approaches, including the heterogeneous ensemble we propose in this paper, are capable of outperforming an SVM classifier (implemented with LibSVM), even when both kernel selection and SVM parameters are carefully tuned for each dataset

Many Local Pattern Texture Features: Which Is Better for Image-Based Multilabel Human Protein Subcellular Localization Classification?

Human protein subcellular location prediction can provide critical knowledge for understanding a protein’s function. Since significant progress has been made on digital microscopy, automated image-based protein subcellular location classification is urgently needed. In this paper, we aim to investigate more representative image features that can be effectively used for dealing with the multilabel subcellular image samples. We prepared a large multilabel immunohistochemistry (IHC) image benchmark from the Human Protein Atlas database and tested the performance of different local texture features, including completed local binary pattern, local tetra pattern, and the standard local binary pattern feature. According to our experimental results from binary relevance multilabel machine learning models, the completed local binary pattern, and local tetra pattern are more discriminative for describing IHC images when compared to the traditional local binary pattern descriptor. The combination of these two novel local pattern features and the conventional global texture features is also studied. The enhanced performance of final binary relevance classification model trained on the combined feature space demonstrates that different features are complementary to each other and thus capable of improving the accuracy of classification

3D Shape Descriptor-Based Facial Landmark Detection: A Machine Learning Approach

Facial landmark detection on 3D human faces has had numerous applications in the literature such as establishing point-to-point correspondence between 3D face models which is itself a key step for a wide range of applications like 3D face detection and authentication, matching, reconstruction, and retrieval, to name a few. Two groups of approaches, namely knowledge-driven and data-driven approaches, have been employed for facial landmarking in the literature. Knowledge-driven techniques are the traditional approaches that have been widely used to locate landmarks on human faces. In these approaches, a user with sucient knowledge and experience usually denes features to be extracted as the landmarks. Data-driven techniques, on the other hand, take advantage of machine learning algorithms to detect prominent features on 3D face models. Besides the key advantages, each category of these techniques has limitations that prevent it from generating the most reliable results. In this work we propose to combine the strengths of the two approaches to detect facial landmarks in a more ecient and precise way. The suggested approach consists of two phases. First, some salient features of the faces are extracted using expert systems. Afterwards, these points are used as the initial control points in the well-known Thin Plate Spline (TPS) technique to deform the input face towards a reference face model. Second, by exploring and utilizing multiple machine learning algorithms another group of landmarks are extracted. The data-driven landmark detection step is performed in a supervised manner providing an information-rich set of training data in which a set of local descriptors are computed and used to train the algorithm. We then, use the detected landmarks for establishing point-to-point correspondence between the 3D human faces mainly using an improved version of Iterative Closest Point (ICP) algorithms. Furthermore, we propose to use the detected landmarks for 3D face matching applications

An Empirical Study of Different Approaches for Protein Classification

Many domains would benefit from reliable and efficient systems for automatic protein classification. An area of particular interest in recent studies on automatic protein classification is the exploration of new methods for extracting features from a protein that work well for specific problems. These methods, however, are not generalizable and have proven useful in only a few domains. Our goal is to evaluate several feature extraction approaches for representing proteins by testing them across multiple datasets. Different types of protein representations are evaluated: those starting from the position specific scoring matrix of the proteins (PSSM), those derived from the amino-acid sequence, two matrix representations, and features taken from the 3D tertiary structure of the protein. We also test new variants of proteins descriptors. We develop our system experimentally by comparing and combining different descriptors taken from the protein representations. Each descriptor is used to train a separate support vector machine (SVM), and the results are combined by sum rule. Some stand-alone descriptors work well on some datasets but not on others. Through fusion, the different descriptors provide a performance that works well across all tested datasets, in some cases performing better than the state-of-the-art

FER Based on Fusion Features of CS-LSMP

Local feature descriptors play a fundamental and important role in facial expression recognition. This paper presents a new descriptor, Center-Symmetric Local Signal Magnitude Pattern (CS-LSMP), which is used for extracting texture features from facial images. CS-LSMP operator takes signal and magnitude information of local regions into account compared to conventional LBP-based operators. Additionally, due to the limitation of single feature extraction method and in order to make full advantages of different features, this paper employs CS-LSMP operator to extract features from Orientational Magnitude Feature Maps (OMFMs), Positive-and-Negative Magnitude Feature Maps (PNMFMs), Gabor Feature Maps (GFMs) and facial patches (eyebrows-eyes, mouths) for obtaining fused features. Unlike HOG, which only retains horizontal and vertical magnitudes, our work generates Orientational Magnitude Feature Maps (OMFMs) by expanding multi-orientations. This paper build two distinct feature maps by dividing local magnitudes into two groups, i.e., positive and negative magnitude feature maps. The generated Gabor Feature Maps (GFMs) are also grouped to reduce the computational complexity. Experiments on the JAFFE and CK+ facial expression datasets showed that the proposed framework achieved significant improvement and outperformed some state-of-the-art methods

Automatic citrus canker detection from leaf images captured in field

Citrus canker, a bacterial disease of citrus tree leaves, causes significant damage to citrus production worldwide. Effective and fast disease detection methods must be undertaken to minimize the losses of citrus canker infection. In this paper, we present a new approach based on global features and zone-based local features to detect citrus canker from leaf images collected in field which is more difficult than the leaf images captured in labs. Firstly, an improved AdaBoost algorithm is used to select the most significant features of citrus lesions for the segmentation of the lesions from their background. Then a canker lesion descriptor is proposed which combines both color and local texture distribution of canker lesion zones suggested by plant phytopathologists. A two-level hierarchical detection structure is developed to identify canker lesions. Thirdly, we evaluate the proposed method and its comparison with other approaches, and the experimental results show that the proposed approach achieves similar classification accuracy as human experts

Local Binary Patterns in Focal-Plane Processing. Analysis and Applications

Feature extraction is the part of pattern recognition, where the sensor data is transformed into a more suitable form for the machine to interpret. The purpose of this step is also to reduce the amount of information passed to the next stages of the system, and to preserve the essential information in the view of discriminating the data into different classes. For instance, in the case of image analysis the actual image intensities are vulnerable to various environmental effects, such as lighting changes and the feature extraction can be used as means for detecting features, which are invariant to certain types of illumination changes. Finally, classification tries to make decisions based on the previously transformed data. The main focus of this thesis is on developing new methods for the embedded feature extraction based on local non-parametric image descriptors. Also, feature analysis is carried out for the selected image features. Low-level Local Binary Pattern (LBP) based features are in a main role in the analysis. In the embedded domain, the pattern recognition system must usually meet strict performance constraints, such as high speed, compact size and low power consumption. The characteristics of the final system can be seen as a trade-off between these metrics, which is largely affected by the decisions made during the implementation phase. The implementation alternatives of the LBP based feature extraction are explored in the embedded domain in the context of focal-plane vision processors. In particular, the thesis demonstrates the LBP extraction with MIPA4k massively parallel focal-plane processor IC. Also higher level processing is incorporated to this framework, by means of a framework for implementing a single chip face recognition system. Furthermore, a new method for determining optical flow based on LBPs, designed in particular to the embedded domain is presented. Inspired by some of the principles observed through the feature analysis of the Local Binary Patterns, an extension to the well known non-parametric rank transform is proposed, and its performance is evaluated in face recognition experiments with a standard dataset. Finally, an a priori model where the LBPs are seen as combinations of n-tuples is also presentedSiirretty Doriast