Rotation-invariant features based on directional coding for texture classification

Ouslimani, Farida; Ouslimani, A.; Ameur, Zohra

doi:10.1007/s00521-018-3462-9

Cited by 12 publications

(2 citation statements)

References 23 publications

(27 reference statements)

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“…Geng et al [42] proposed two approaches: The Keypoints-Preserving-SIFT (KPSIFT) and Partial-Descriptor-SIFT (PDSIFT); the approaches achieved good results in face recognition. Rotation-invariant features based on directional coding proposed by Ousliman et al [43] for texture classification; this method can obtain an average recognition accuracy of 90.63% on the YALE B database.…”

Section: Related Workmentioning

confidence: 99%

New Approaches for Automatic Face Recognition Based on Deep Learning Models and Local Handcrafted ALTP

Hattab¹,

Behloul²

2018

ICST Transactions on Scalable Information Systems

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Face recognition is one of the most important topics in biometrics, where it achieved great success under controlled scenarios. Still, its accuracy degraded significantly in unconstrained conditions. To meet this challenge, we proposed a handcraft method based on extracting important regions from the face image. We have been using Scale-Invariant Feature Transform (SIFT) besides the Adaptive Local Ternary Patterns (ALTP). We have achieved an accuracy of 99.75% on the ORL database and 94.70% on the FERET database. Then, we proposed a second method based on deep learning to achieve more accurate face recognition. The deep learning models failed to achieve a high accuracy rate because they require a large amount of training data. We used firstly Data Augmentation to solve this failure. However, this solution does not show high performance. Secondly, our proposed ImageNet pre-trained AlexNet-v2 and VGG16 models with LinearSVC increased the accuracy rate to 100% for the both databases.

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Section: Related Workmentioning

confidence: 99%

New Approaches for Automatic Face Recognition Based on Deep Learning Models and Local Handcrafted ALTP

Hattab¹,

Behloul²

2018

ICST Transactions on Scalable Information Systems

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“…La creaci ón de descriptores de textura eficientes para caracterizar la imagen es esencial en los trabajos relacionados con la recuperaci ón y clasificaci ón de imágenes basadas en la textura García-Olalla et al, 2018;Kwitt and Uhl, 2008). 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.…”

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 Nonparametric Pooling Operator Capable of Texture Extraction

Vigneron¹,

Maaref²

2023

Lecture Notes in Computer Science

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Rotation-invariant features based on directional coding for texture classification

Cited by 12 publications

References 23 publications

New Approaches for Automatic Face Recognition Based on Deep Learning Models and Local Handcrafted ALTP

New Approaches for Automatic Face Recognition Based on Deep Learning Models and Local Handcrafted ALTP

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

A Nonparametric Pooling Operator Capable of Texture Extraction

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