Journal ArticleDOI
Local Directional Texture Pattern image descriptor
TLDR
This paper proposes a new feature descriptor named Local Directional Texture Pattern (LDTP) that is versatile, as it allows us to distinguish person's expressions, and different landscapes scenes, and uses Principal Component Analysis to reduce the dimension of the multilevel feature set.About:
This article is published in Pattern Recognition Letters.The article was published on 2015-01-01. It has received 89 citations till now. The article focuses on the topics: GLOH & Local binary patterns.read more
Citations
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Journal ArticleDOI
Local directional ternary pattern: A New texture descriptor for texture classification
TL;DR: The experiments carried out on nine publicly available texture datasets demonstrated that the proposed LDTP descriptor achieves classification performance, which is competitive or better than several recent and old state-of-the-art LBP variants.
Journal ArticleDOI
Attractive-and-repulsive center-symmetric local binary patterns for texture classification
TL;DR: A new modeling of the conventional LBP operator for texture classification named Attractive-and-Repulsive Center-Symmetric Local Binary Patterns and its variants and multiscale ARCS-LBP descriptor is presented, showing that the proposed operators can achieve impressive classification accuracy.
Journal ArticleDOI
Lung nodule classification using artificial crawlers, directional texture and support vector machine
Bruno Rodrigues Froz,Antonio Oseas de Carvalho Filho,Aristófanes Corrêa Silva,Anselmo Cardoso de Paiva,Rodolfo Acatauassú Nunes,Marcelo Gattass +5 more
TL;DR: The proposed methodology to classify lung nodule and non-nodule candidates based on computed tomography images using texture features is a useful tool for specialists in the detection of nodules and can be extended to applications based on images with other contexts.
Journal ArticleDOI
Histogram-based local descriptors for facial expression recognition (FER): A comprehensive study
Cigdem Turan,Kin-Man Lam +1 more
TL;DR: Different studies of the visual features for FER are brought together by evaluating their performances under the same experimental setup, and critically reviewing various classifiers making use of the local descriptors.
Journal ArticleDOI
A novel pipeline framework for multi oriented scene text image detection and recognition
TL;DR: A convolutional neural network-based pipeline is introduced to obtain high-level visual features and improve text detection and recognition efficiency and a pipeline framework for character recognition that is robust to irregular (curve and vertical) text is proposed.
References
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Journal ArticleDOI
Support-Vector Networks
Corinna Cortes,Vladimir Vapnik +1 more
TL;DR: High generalization ability of support-vector networks utilizing polynomial input transformations is demonstrated and the performance of the support- vector network is compared to various classical learning algorithms that all took part in a benchmark study of Optical Character Recognition.
Book
Principal Component Analysis
TL;DR: In this article, the authors present a graphical representation of data using Principal Component Analysis (PCA) for time series and other non-independent data, as well as a generalization and adaptation of principal component analysis.
Proceedings ArticleDOI
Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
TL;DR: This paper presents a method for recognizing scene categories based on approximate global geometric correspondence that exceeds the state of the art on the Caltech-101 database and achieves high accuracy on a large database of fifteen natural scene categories.
A Practical Guide to Support Vector Classication
TL;DR: A simple procedure is proposed, which usually gives reasonable results and is suitable for beginners who are not familiar with SVM.
Journal ArticleDOI
Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope
Aude Oliva,Antonio Torralba +1 more
TL;DR: The performance of the spatial envelope model shows that specific information about object shape or identity is not a requirement for scene categorization and that modeling a holistic representation of the scene informs about its probable semantic category.