In this paper, we address the general problem of image/object categorization with a novel approach referred to as Bag-of-Scenes (BoS).Our approach is efficient for low semantic applications such as texture classification as well as for higher semantic tasks such as natural scenes recognition or fine-grained visual categorization (FGVC). It is based on the widely used combination of i) Sparse co...

In this paper we propose a new approach for false positive reduction in the field of mammographic mass detection. The goal is to distinguish between the true recognized masses and the ones which actually are normal parenchyma. Our proposal is based on Local Binary Patterns (LBP) for representing salient micro-patterns and preserving at the same time the spatial structure of the masses. Once the...

This paper proposes a novel approach for facial expression recognition by boosting Local Binary Patterns (LBP) based classifiers. Low-cost LBP features are introduced to effectively describle local features of face images. A novel learning procedure, Conditional Mutual Infomation based Boosting (CMIB), is proposed. CMIB learns a sequence of weak classifiers that maximize their mutual informatio...

Binary feature descriptors have been widely used in computer vision field due to their excellent discriminative power and strong robustness, and local binary patterns (LBP) and its variations have proven that they are effective face descriptors. However, the forms of such binary feature descriptors are predefined in the hand-crafted way, which requires strong domain knowledge to design them. In...

This paper proposes a novel approach for facial expression recognition by boosting Local Binary Patterns (LBP) based classifiers. Low-cost LBP features are introduced to effectively describle local features of face images. A novel learning procedure, Conditional Mutual Information based Boosting (CMIB), is proposed. CMIB learns a sequence of weak classifiers that maximize their mutual informati...

This paper presents a new unified framework for texture descriptors such as Local Binary Patterns (LBP) and Maximum Response 8 (MR8) that are based on histograms of local pixel neighborhood properties. This framework is enabled by a novel filter based approach to the LBP operator which shows that it can be seen as a special filter based texture operator. Using the proposed framework, the filter...

In this paper, we employ several subspace representations (PCA, UDP, KCFA, and KDA) on our proposed discrete transform encoded local binary patterns (DT-LBP) to match periocular region on a large data set such as NIST’s Face Recognition Grand Challenge (FRGC) ver2.0 database. We strictly follow FRGC EXPERIMENT 4 protocol which involves 1-to-1 matching of 8,014 uncontrolled probe periocular imag...

Face representation is one of the open problems in face detection. The recently proposed Multi-Block Local Binary Patterns (MBLBP) representation has shown good results for this purpose. Although dissimilarity representation has proved to be effective in a variety of pattern recognition problems, to the best of our knowledge, it has never been used for face detection. In this paper, we propose ...

In this paper, we propose a new finger biometric method. Infrared finger images are first captured, and then feature extraction is performed using a modified gaussian high-pass filter through binarization, local binary pattern (LBP), and local derivative pattern (LDP) methods. Infrared finger images include the multimodal features of finger veins and finger geometries. Instead of extracting eac...

The local properties of an image can be acquired in many ways. Local Binary Patterns(LBP) operator is one among them in which a centre pixel is referenced with the neighboring pixels to obtain a feature vector. However, the directions are not considered in this method. The Directional Local Extrema Patterns(DLEP) are used to encode the relationship between the reference pixel and its neighbors ...