A novel and accurate chess pattern for automated texture classification

Tuncer, Türker; Doğan, Şengül; Ataman, Volkan

doi:10.1016/j.physa.2019.122584

Cited by 14 publications

(7 citation statements)

References 51 publications

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“…In this section, the existing feature descriptors namely Chess Pattern (CP) [57], Local Gradient Coding (LGC) [55] and its variants are presented.…”

Section: Existing Feature Descriptorsmentioning

confidence: 99%

“…Tuncer et al [57] proposed CP, a local texture-based feature descriptor, developed using chess game rules for texture recognition. With reference to the center pixel (G c ) in a 5 × 5 neighborhood, CP logically places chessmen (Rook, Bishop and Knight) in possible positions following chess game rules.…”

Section: Cpmentioning

confidence: 99%

“…For accurate classification in FER, it is essential to capture those minute information related to specific expressions. Based on the literature studies, the texture-based feature descriptors have been proven to be effective for extracting valuable features from a facial image by manipulating the neighborhood pixel relationship for detecting and capturing minute details in an image [35,56,57]. In FER systems, the most significant problem is to extract effective and meaningful patterns from the facial images [56].…”

Section: Introductionmentioning

confidence: 99%

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Modified chess patterns: handcrafted feature descriptors for facial expression recognition

Kartheek

Prasad

Bhukya

2021

Complex Intell. Syst.

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Facial expressions are predominantly important in the social interaction as they convey the personal emotions of an individual. The main task in Facial Expression Recognition (FER) systems is to develop feature descriptors that could effectively classify the facial expressions into various categories. In this work, towards extracting distinctive features, Radial Cross Pattern (RCP), Chess Symmetric Pattern (CSP) and Radial Cross Symmetric Pattern (RCSP) feature descriptors have been proposed and are implemented in a 5 $$\times $$ × 5 overlapping neighborhood to overcome some of the limitations of the existing methods such as Chess Pattern (CP), Local Gradient Coding (LGC) and its variants. In a 5 $$\times $$ × 5 neighborhood, the 24 pixels surrounding the center pixel are arranged into two groups, namely Radial Cross Pattern (RCP), which extracts two feature values by comparing 16 pixels with the center pixel and Chess Symmetric Pattern (CSP) extracts one feature value from the remaining 8 pixels. The experiments are conducted using RCP and CSP independently and also with their fusion RCSP using different weights, on a variety of facial expression datasets to demonstrate the efficiency of the proposed methods. The results obtained from the experimental analysis demonstrate the efficiency of the proposed methods.

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“…In this section, the existing feature descriptors namely Chess Pattern (CP) [57], Local Gradient Coding (LGC) [55] and its variants are presented.…”

Section: Existing Feature Descriptorsmentioning

confidence: 99%

Section: Cpmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Modified chess patterns: handcrafted feature descriptors for facial expression recognition

Kartheek

Prasad

Bhukya

2021

Complex Intell. Syst.

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“…The need to design an effective local texture descriptor with high discrimination capability is no longer to be demonstrated. Indeed, the development of methods based on local texture descriptors in pattern recognition continue to be designed still today, e.g., median local ternary patterns (MLTP) [50], averaged local binary patterns (ALBP) [58], repulsive-andattractive local binary gradient contours (RALBGC) [52], local concave-and-convex micro-structure (LCCMSP) [49], attractive-and-repulsive center-symmetric local binary patterns (ARCS-LBP) [63], multi-direction local binary pattern (MDLBP) [64], improved local ternary patterns (ILTP) [55], quaternionic local angular binary pattern (QLABP) [61], selectively dominant local binary patterns (SDLBP) [62], chess pattern (Chess-pat) [59], multi level directional cross binary patterns (MLD-CBP) [60], synchronized rotation local ternary pattern (SRLTP) [38], oriented star sampling structure based multi-scale ternary pattern (O3S-MTP) [40], pattern of local gravitational force (PLGF) [39] and so on. Even though LBP and its modifications and extensions achieve satisfactory performance, still an alternative technique to enhance the discriminative power in an image for effective texture modeling and representation is essential.…”

Section: Introductionmentioning

confidence: 99%

Directional Neighborhood Topologies Based Multi-Scale Quinary Pattern for Texture Classification

et al. 2020

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This paper ideates a new computationally simple and effective local image feature descriptor, referred to as Directional Neighborhood Topologies based Multi-scale Quinary Pattern (DNT-MQP) for texture description and classification. The essence of DNT-MQP is to encode the structure of local neighborhood by analyzing the differential excitation and directional information using various directional neighborhood topologies and new pattern encoding scheme. We first designed four different versions of single scale DNT-QP features based on four directional neighborhood topologies based sampling sets which are then combined together to build the effective multiscale DNT-MQP model. Unlike some existing parametric methods that employ static thresholds to perform thresholding, the construction process of DNT-MQP includes an automatic mechanism for dynamic thresholds estimation. Thanks to a richer local description ensured by exploiting complementary information resulting from single scale DNT-QP operators' combination, DNT-MQP descriptor has high capability to elicit stable and discriminative feature representation than other local feature descriptors. In addition, DNT-QP has the advantages of computational simplicity in feature extraction and low-dimensionality in feature representation. The effectiveness of DNT-QP is evaluated on sixteen challenging texture datasets and it is found that it maintains a high level of performance stability where the achieved performances are competitive or better than several recent most promising state-of-theart texture descriptors as well as deep learning-based feature extraction approaches. Impressively, DNT-MQP showed good tolerance to rotation as well as illumination, scale and viewpoint changes against certain descriptors which are originally conceived to deal with these challenges. Furthermore, statistical hypothesis testing through Wilcoxon signed rank test is applied to prove the statistical significance of the accuracy improvement obtained in all the datasets.

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“…En la literatura, se han propuesto recientemente muchos descriptores para el análisis de la textura, por ejemplo, en (Ouslimani et al, 2019) se propuso un des-criptor de textura invariante a la rotaci ón para abordar la tarea de clasificaci ón, y Pham (2018) introdujeron un método para la recuperaci ón de la textura utilizando la extracci ón de características multiescala. Para el reconocimiento de imágenes de textura, Tuncer, Dogan and Ertam (2019) utilizaron una red neuronal para la extracci ón de características de textura, y más tarde, introdujeron un novedoso descriptor de imagen local (Tuncer, Dogan and Ataman, 2019) para la extracci ón de características de textura inspirado en el juego de ajedrez. El objetivo principal de todos estos trabajos es satisfacer las demandas de ciertas aplicaciones con respecto a CBIR.…”

Section: Descriptores Para La Recuperaci óN De Texturasunclassified

Image feature representation using deep learning for instance search and scene recognition = Representación de características de la imagen utilizando el aprendizaje profundo para la búsqueda de instancias y el reconocimiento de escenas

Saikia¹

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This thesis investigates the creation of novel algorithms for representing images to address two important areas in the field of computer vision: content-based image retrieval (CBIR) and scene recognition. CBIR can be classified into two types, instance-level retrieval and category-level retrieval, and in this thesis, we address the former. Motivated by our joint work with INCIBE, we build deep learning-based systems that can help Law Enforcement Agencies to match evidences in crime scene investigations, among a wide range of other applications. In particular, we propose two algorithms for CBIR, one based on the colour description of objects and the other one on the texture description of patches on images, and another additional algorithm for scene prediction and retrieval that relies on the combination of local and global scene content.CBIR for instance-level retrieval aims at retrieving images from an image or video database that contain the same object or scene as the one depicted in a query image. We introduce two algorithms to address this task in order to gain robustness against colour and texture variances, respectively. On the one hand, we propose colour neural descriptors that are composed of convolutional neural networks (CNNs) features obtained by combining different colour spaces and colour channels. In contrast to previous works, which rely on fine-tuning pre-trained networks, we compute the proposed descriptors based on the activations generated from a pre-trained CNN without fine-tuning. Also, we take advantage of an object detector to optimize the proposed instance retrieval architecture to generate features at both local and global scales. In addition, we introduce a stride based query expansion technique to retrieve objects from multi-view datasets. Finally, we experimentally demonstrated that the proposed colour neural descriptors obtain state-of-the-art results on the Paris 6K, Revisiting-Paris 6k, INSTRE-M and COIL-100 datasets, with mean average precision of 81.70%, 82.02%, 78.8% and 97.9%, respectively.On the other hand, we focus on the texture properties of images. In crime scene investigations, some clues may come from texture patches of images that do not contain much information about the object contour, like a t-shirt lying on the floor. To define the characteristics of such images, the texture patterns are the prime cues for teriores en tres conjuntos de datos p úblicos: MIT-67 Indoor, NYU-v2 y Hotels-50k. La precisi ón alcanzada (MIT-67 Indoor = 94, 5%, NYU-v2 = 74, 5% y la precisi ón top-1 10, 1% sin oclusi ón y 7,8% con oclusi ón media en el Hotels-50k) demostr ó la eficacia del método propuesto, que también supera significativamente los enfoques del estado del arte existentes.Esta tesis contribuye al desarrollo de métodos para crear descriptores robustos a los cambios de color, textura y punto de vista y presenta marcos para utilizarlos en sistemas CBIR y de reconocimiento de escenas.

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A novel and accurate chess pattern for automated texture classification

Cited by 14 publications

References 51 publications

Modified chess patterns: handcrafted feature descriptors for facial expression recognition

Modified chess patterns: handcrafted feature descriptors for facial expression recognition

Directional Neighborhood Topologies Based Multi-Scale Quinary Pattern for Texture Classification

Image feature representation using deep learning for instance search and scene recognition = Representación de características de la imagen utilizando el aprendizaje profundo para la búsqueda de instancias y el reconocimiento de escenas

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