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Feature engineering forms an important component of machine learning and pattern recognition. It is a fundamental process for off-line writer identification of handwritten documents, which continues to be an interesting subject of research in various forensic and authentication areas. In this work, we propose an efficient, yet computationally and conceptually simple framework for off-line text independent writer identification using local textural features in characterizing the writing style of each writer. These include Local Binary Patterns (LBP), Local Ternary Patterns (LTP), and Local Phase Quantization (LPQ). Our approach focuses on exploiting the writing images at small observation regions where a set of connected component sub-images are cropped and extracted from each handwriting sample (document or set of word/text line images). These connected components are seen as texture images where each one of them is subjected to feature extraction using LBP, LPQ, or LTP. Then, a histogram sequence concatenation is applied to the feature image after dimensionality reduction followed by image subdivision into a number of non-overlapping regions. For classification, the 1-NN (Nearest Neighbor) classifier is used to identify the writer of the questioned samples based on the dissimilarity of feature vectors computed from all components in the writing. Experiments on IFN/ENIT (411 writers/Arabic), AHTID/MW (53 writers/Arabic), CVL (309 writers/English), and IAM (657 writers/English) databases demonstrate that our proposed system outperforms old and recent state-of-the-art writer identification systems on Arabic script, and demonstrates a competitive performance on English ones.

Face recognition becomes an important task performed routinely in our daily lives. This application is encouraged by the wide availability of powerful and low-cost desktop and embedded computing systems, while the need comes from the integration in too much real world systems including biometric authentication, surveillance, human-computer interaction, and multimedia management. Moreover, face recognition technology is now adopted in new intelligent systems and devices like smart-phones, which impose some constraints related to the complexity and execution time of the recognition process. This fact brings new challenges and gives much more area to extend the ongoing researches. This research field experienced the development of many methods and architectures aiming at producing face recognition systems which are efficient in terms of precision, robustness and computation time. In the same context, this article proposes a new feature descriptor referred to as Mixed Neighborhood Topology Cross Decoded Patterns (MNTCDP) as an effective face descriptor, The proposed handcrafted descriptor fulfills the needs of current face recognition applications and can be integrated in different platforms, requiring simple, robust and computationally low algorithms. Instead of heuristic code constructions, MNTCDP is built using new neighborhood topology and new pattern encoding scheme, which have high ability to extract discriminative and stable face representation. The adopted face recognition system consists of three stages: (1) face detection and alignment to normalize the input images to a common form if needed; (2) feature extraction using the proposed MNTCDP descriptor and (3) face recognition through a supervised image classification task using the simple K-Nearest Neighbors classifier. Simulated experiments on ORL, YALE, Extended Yale B, FERET and AR datasets acquired under different illumination conditions or facial expressions show that the proposed MNTCDP descriptor presents high performance ability in classifying face images. MNTCDP demonstrates superior performance than a large number of recent state-of-the-art LBP variants and deep learning methods, as well as recent most promising works of the literature.

Feature engineering is fundamental in applied machine learning. It plays a major role in writer identification of handwritten documents, which has been an active area of research in the literature. In this paper, we propose a conceptually simple, yet high-quality and computationally efficient descriptor referred to as block wise local binary count (BW-LBC) for offline text independent writer identification of handwritten documents. The proposed BW-LBC operator characterizes the writing style of each writer by a set of histograms calculated from all the connected components in the writing. Each histogram is constructed by calculating the occurrence distribution of pixels corresponding to the writing within small blocks in each connected component extracted and cropped from the input handwriting sample (document or set of words/text lines). Specifically, for a given connected component divided into N × N non-overlapping blocks, the appearance probability of writing pixels in the block number i corresponds to the histogram bin number i in the produced corresponding histogram of N × N bins. The samples are classified according to their normalized histogram feature vectors through the nearest-neighbor rule (1-NN) using the Hamming distance. Extensive experiments performed on four challenging handwritten databases (IFN/ENIT, AHTID/MW, CVL and IAM) containing handwritten texts in Arabic and English languages, show that the proposed system using the BW-LBC operator demonstrates superior performance on the Arabic databases (i.e., AHTID/MW and IFN/ENIT) and competitive performance on the English scripts compared to the old and recent state-of-the-art writer identification approaches.

Automatic building detection in orthophotos via a machine learning approach.Flexible framework that exploits supervised learning.Applying the covariance descriptor to the building detection problem.An extended performance study of several combination segmentation-descriptor.Classification performance is obtained with K-NN, Partial Least Square and SVM. Building detection from aerial images has many applications in fields like urban planning, real-estate management, and disaster relief. In the last two decades, a large variety of methods on automatic building detection have been proposed in the remote sensing literature. Many of these approaches make use of local features to classify each pixel or segment to an object label, therefore involving an extra step to fuse pixelwise decisions. This paper presents a generic framework that exploits recent advances in image segmentation and region descriptors extraction for the automatic and accurate detection of buildings on aerial orthophotos. The proposed solution is supervised in the sense that appearances of buildings are learnt from examples. For the first time in the context of building detection, we use the matrix covariance descriptor, which proves to be very informative and compact. Moreover, we introduce a principled evaluation that allows selecting the best pair segmentation algorithm-region descriptor for the task of building detection. Finally, we provide a performance evaluation at pixel level using different classifiers. This evaluation is conducted over 200 buildings using different segmentation algorithms and descriptors. The performance analysis quantifies the quality of both the image segmentation and the descriptor used. The proposed approach presents several advantages in terms of scalability, suitability and simplicity with respect to the existing methods. Furthermore, the proposed scheme (detection chain and evaluation) can be deployed for detecting multiple object categories that are present in images and can be used by intelligent systems requiring scene perception and parsing such as intelligent unmanned aerial vehicle navigation and automatic 3D city modeling.

In this paper, we develop a new efficient graph construction algorithm that is useful for many learning tasks. Unlike the main stream for graph construction, our proposed data self-representativeness approach simultaneously estimates the graph structure and its edge weights through sample coding. Compared with the recent @?"1 graph based on sparse coding, our proposed objective function has an analytical solution (based on self-representativeness of data) and thus is more efficient. This paper has two main contributions. Firstly, we introduce a principled Two Phase Weighted Regularized Least Square graph construction method. Secondly, the obtained data graph is used, in a semi-supervised context, in order to categorize detected objects in outdoor and indoor scenes using Local Binary Patterns as image descriptors. In many previous works, LBP descriptors (histograms) were used as feature vectors for object detection and recognition. However, our work exploits them in order to construct adaptive graphs using a self-representativeness coding. The experiments show that the proposed method can outperform competing methods.