Robust Adaptive Median Binary Pattern for noisy texture classification and retrieval

Texture is an important cue for different computer vision tasks and applications. Local Binary Pattern (LBP) is considered one of the best yet efficient texture descriptors. However, LBP has some notable limitations, mostly the sensitivity to noise. In this paper, we address these criteria by introducing a novel texture descriptor, Robust Adaptive Median Binary Pattern (RAMBP). RAMBP based on classification process of noisy pixels, adaptive analysis window, scale analysis and image regions median comparison. The proposed method handles images with high noisy textures, and increases the discriminative properties by capturing microstructure and macrostructure texture information. The proposed method has been evaluated on popular texture datasets for classification and retrieval tasks, and under different high noise conditions. Without any train or prior knowledge of noise type, RAMBP achieved the best classification compared to state-of-the-art techniques. It scored more than $90\%$ under $50\%$ impulse noise densities, more than $95\%$ under Gaussian noised textures with standard deviation $\sigma = 5$, and more than $99\%$ under Gaussian blurred textures with standard deviation $\sigma = 1.25$. The proposed method yielded competitive results and high performance as one of the best descriptors in noise-free texture classification. Furthermore, RAMBP showed also high performance for the problem of noisy texture retrieval providing high scores of recall and precision measures for textures with high levels of noise

Major Factors That Influence School Failure in the Northern Region of Morocco (Fez-Boulemane As A Case Study)

AbstractThis study examines the associations between home-individual and school-related factors along with a focus on school failure of 431 students who are randomly sampled from nine urban public Junior high schools. The results show that among individual students some characteristics such as gender, motivation, positive attitudes towards school, and work status are significantly associated with school failure. In addition, some family-related aspects like the parents’ educational level, perceptions about relationship with parents and surrounding circumstances at home are also considerably related to the proportion of school failure. As for school characteristics, the present study proves that they have no effect on the rate of school failure

Adaptive Median Binary Patterns for Texture Classification

Abstract-This paper addresses the challenging problem of recognition and classification of textured surfaces under illumination variation, geometric transformations and noisy sensor measurements. We propose a new texture operator, Adaptive Median Binary Patterns (AMBP) that extends our previous Median Binary Patterns (MBP) texture feature. The principal idea of AMBP is to hash small local image patches into a binary pattern texton by fusing MBP and Local Binary Patterns (LBP) operators combined with using self-adaptive analysis window sizes to better capture invariant microstructure information while providing robustness to noise. The AMBP scheme is shown to be an effective mechanism for non-parametric learning of spatially varying image texture statistics. The local distribution of rotation invariant and uniform binary pattern subsets extended with more global joint information are used as the descriptors for robust texture classification. The AMBP is shown to outperform recent binary pattern and filtering-based texture analysis methods on two large texture corpora (CUReT and KTH TIPS2-b) with and without additive noise. The AMBP method is slightly superior to the best techniques in the noiseless case but significantly outperforms other methods in the presence of impulse noise

Reconnaissance invariante par rotation de textures par des chaînes de relations locales

Les structures locales constituent un élément essentiel dans la description de textures. L'extraction d'une information locale pertinente permet d'augmenter les performances de reconnaissance de texture. Les transformations géométriques affectent en général les structures locales ce qui rend les techniques basées sur ce type d'information vulnérable. Dans cet article, nous nous intéressons à ce problème et plus particulièrement celui de la rotation. Nous avons proposé récemment une méthode efficace de caractérisation de textures qui a prouvé une bonne efficacité dans la classification de textures. En revanche, cette méthode n'est pas invariante à la rotation. Le but de ce travail est de remédier à ce problème en utilisant les techniques d'apprentissage supervisé. La méthode du Séparateur à Vaste Marge a été employée à cette fin. Les expériences effectuées ont montré que l'apprentissage permet de réduire les erreurs de reconnaissance et par conséquent augmenter les performances du système

Joint Adaptive Median Binary Patterns for texture classification

a b s t r a c t This paper addresses the challenging problem of the recognition and classification of textured surfaces given a single instance acquired under unknown pose, scale and illumination conditions. We propose a novel texture descriptor, the Adaptive Median Binary Pattern (AMBP) based on an adaptive analysis window of local patterns. The principal idea of the AMBP is to convert a small local image patch to a binary pattern using adaptive threshold selection that switches between the central pixel value as used in the Local Binary Pattern (LBP) and the median as in Median Binary Pattern (MBP), but within a variable sized analysis window depending on the local microstructure of the texture. The variability of the local adaptive window is included as joint information to increase the discriminative properties. A new multiscale scheme is also proposed in this paper to handle the texture resolution problem. AMBP is evaluated in relation to other recent binary pattern techniques and many other texture analysis methods on three large texture corpora with and without noise added, CUReT, Outex_TC00012 and KTH_TIPS2. Generally, the proposed method performs better than the best state-of-the-art techniques in the noiseless case and significantly outperforms all of them in the presence of impulse noise

CARACTERISATION DE TEXTURES ET SEGMENTATION POUR LA RECHERCHE D'IMAGES PAR LE CONTENU

This thesis describes the design and realization of a complete processing chain for content based image retrieval (CBIR). The study allows to define some "limited semantics" with respect to the user's satisfaction from the system response. The image is decomposed on visual entities to obtain interactions between them, allowing to reach higher levels of abstraction. We have addressed three points in the chain : reliable region-detection, region characterization and then similarity measure. We have modified a Fuzzy C-means by incorporating the spatial and multiresolution information into the objective function. Therefore, the classification of a given point is forced to follow both neighbors and ancestors in a pyramidal representation. Two methods are proposed which exploit Peano scans to coding region features. The first one is based on a grammatical representation of the pixels neighbourhood called motif. The second method modifies the spectrum before to apply Gabor filters. The image signature consists of a list of visual entities containing features. The similarity measure between two images turns into a graph matching problem. We have elaborated a technique that allows a bidirectional matching from query to target and vice versa. A high priority is assigned to those elements which prefer mutually. Each part of the system is evaluated and tested independently then incorporated into the CBIR application. The evaluation of CBIR in terms of "recall-precision" shows that the proposed methods perform better than classical ones, such as grey level co-occurrence matrix and Gabor filters. To open on further extensions and suggest the generality of our method, the conclusion deals with extending it to the situation assessment in car driving, with limited tuning of parameters.Dans cette thèse nous avons élaboré puis automatisé une chaîne complète de recherche d'image par le contenu. Ceci nous a permis de définir une "sémantique limitée" relative à la satisfaction de l'utilisateur quant à la réponse du système. Notre approche est locale c'est-à-dire basée sur les régions de l'image. La décomposition en entités visuelles permet d'exhiber des interactions entres celles-ci et du coup faciliter l'accès à un niveau d'abstraction plus élevé. Nous avons considéré plus particulièrement trois points de la chaîne : l'extraction de régions fiables, leur caractérisation puis la mesure de similarité. Nous avons mis au point une méthode de type C-moyennes floues avec double contrainte spatiale et pyramidale. La classification d'un pixel donné est contrainte à suivre le comportement de ses voisins dans le plan de l'image et de ses ancêtres dans la pyramide. Pour la caractérisation des régions deux méthodes ont été proposées basées sur les courbes de Peano. La première repose sur un principe grammatical et la deuxième manipule le spectre par l'utilisation des filtres de Gabor. La signature de l'image requête ou cible consiste en une liste d'entités visuelles. La mesure de similarité entre entités guide l'appariement. Nous avons élaboré une méthode basée sur la mise en correspondance dans les deux sens, requête vers cible et vice versa, afin de donner indépendamment une grande priorité aux éléments qui se préfèrent mutuellement. Chaque partie du système a été testée et évaluée séparément puis ramenée à l'application CBIR. Notre technique a été évaluée sur des images aériennes (et ou satellitaires). Les résultats en terme de "rappel-précision" sont satisfaisants comparé notamment aux méthodes classiques type matrice de co-occurrence des niveaux de gris et Gabor standard. Pour ouvrir sur de futures extensions et montrer la généralité de notre méthode, la conclusion explique sa transposition à la recherche de situations en conduite automobile, au prix d'une adaptation limitée des paramètres