Relevant bibliographies by topics / Pyramid histogram of oriented gradients (PHOG)

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Conference papers

Academic literature on the topic 'Pyramid histogram of oriented gradients (PHOG)'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Pyramid histogram of oriented gradients (PHOG).'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Journal articles on the topic "Pyramid histogram of oriented gradients (PHOG)"

1

Mutia, Cut, Fitri Arnia, and Rusdha Muharar. "Improving the Performance of CBIR on Islamic Women Apparels Using Normalized PHOG." Bulletin of Electrical Engineering and Informatics 6, no. 3 (September 1, 2017): 271–80. http://dx.doi.org/10.11591/eei.v6i3.657.

Full text
Abstract:
The designs of Islamic women apparels is dynamically changing, which can be shown by emerging of online shops selling clothing with fast updates of newest models. Traditionally, buying the clothes online can be done by querying the keywords to the retrieval system. The approach has a drawback that the keywords cannot describe the clothes designs precisely. Therefore, a searching based on content–known as content-based image retrieval (CBIR)–is required. One of the features used in CBIR is the shape. This article presents a new normalization approach to the Pyramid Histogram of Oriented Gradients (PHOG) as a mean for shape feature extraction of women Islamic clothing in a retrieval system. We refer to the proposed approach as normalized PHOG (NPHOG). The Euclidean distance measured the similarity of the clothing. The performance of the system was evaluated by using 340 clothing images, comprised of four clothing categories, 85 images for each category: blouse-pants, long dress, outerwear, and tunic. The recall and precision parameters measured the retrieval performance; the Histogram of Oriented Gradients (HOG) and PHOG were the methods for comparison. The experiments showed that NPHOG improved the HOG and PHOG performance in three clothing categories.
APA, Harvard, Vancouver, ISO, and other styles
2

Gour, Neha, and Pritee Khanna. "Automated glaucoma detection using GIST and pyramid histogram of oriented gradients (PHOG) descriptors." Pattern Recognition Letters 137 (September 2020): 3–11. http://dx.doi.org/10.1016/j.patrec.2019.04.004.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Yan, Chao, Frans Coenen, and Bai Ling Zhang. "Driving Posture Recognition by Joint Application of Motion History Image and Pyramid Histogram of Oriented Gradients." Advanced Materials Research 846-847 (November 2013): 1102–5. http://dx.doi.org/10.4028/www.scientific.net/amr.846-847.1102.

Full text
Abstract:
This paper presents a novel approach to vision-based driving posture recognition. A driving action dataset was prepared by a side-mounted camera looking at a driver's left profile. The driving actions, including operating the shift lever, talking on a cell phone, eating and smoking, are decomposed into a number of predefined action primitives, which include operation of the shift lever, interaction with the drivers head and interaction with the dashboard. A global grid-based representation for the action primitives was emphasized, which first generate the silhouette shape from the motion history image, followed by application of the Pyramid Histogram of Oriented Gradients (PHOG) for more discriminating characterization. The random forest (RF) classifier was then exploited to classify the action primitives. Comparisons with some other commonly applied classifiers, such as kNN, multiple layer perceptron (MLP) and support vector machine (SVM), were provided. Classification accuracy is over 95% for the RF classifier in holdout experiment on the four manually decomposed driving actions.
APA, Harvard, Vancouver, ISO, and other styles
4

Yan, Chao, Frans Coenen, and Bailing Zhang. "Driving Posture Recognition by Joint Application of Motion History Image and Pyramid Histogram of Oriented Gradients." International Journal of Vehicular Technology 2014 (January 28, 2014): 1–11. http://dx.doi.org/10.1155/2014/719413.

Full text
Abstract:
In the field of intelligent transportation system (ITS), automatic interpretation of a driver’s behavior is an urgent and challenging topic. This paper studies vision-based driving posture recognition in the human action recognition framework. A driving action dataset was prepared by a side-mounted camera looking at a driver’s left profile. The driving actions, including operating the shift lever, talking on a cell phone, eating, and smoking, are first decomposed into a number of predefined action primitives, that is, interaction with shift lever, operating the shift lever, interaction with head, and interaction with dashboard. A global grid-based representation for the action primitives was emphasized, which first generate the silhouette shape from motion history image, followed by application of the pyramid histogram of oriented gradients (PHOG) for more discriminating characterization. The random forest (RF) classifier was then exploited to classify the action primitives together with comparisons to some other commonly applied classifiers such as kNN, multiple layer perceptron, and support vector machine. Classification accuracy is over 94% for the RF classifier in holdout and cross-validation experiments on the four manually decomposed driving actions.
APA, Harvard, Vancouver, ISO, and other styles
5

Zhao, Zheng-Yang, Wen-Zhun Huang, Xin-Ke Zhan, Jie Pan, Yu-An Huang, Shan-Wen Zhang, and Chang-Qing Yu. "An Ensemble Learning-Based Method for Inferring Drug-Target Interactions Combining Protein Sequences and Drug Fingerprints." BioMed Research International 2021 (April 24, 2021): 1–12. http://dx.doi.org/10.1155/2021/9933873.

Full text
Abstract:
Identifying the interactions of the drug-target is central to the cognate areas including drug discovery and drug reposition. Although the high-throughput biotechnologies have made tremendous progress, the indispensable clinical trials remain to be expensive, laborious, and intricate. Therefore, a convenient and reliable computer-aided method has become the focus on inferring drug-target interactions (DTIs). In this research, we propose a novel computational model integrating a pyramid histogram of oriented gradients (PHOG), Position-Specific Scoring Matrix (PSSM), and rotation forest (RF) classifier for identifying DTIs. Specifically, protein primary sequences are first converted into PSSMs to describe the potential biological evolution information. After that, PHOG is employed to mine the highly representative features of PSSM from multiple pyramid levels, and the complete describers of drug-target pairs are generated by combining the molecular substructure fingerprints and PHOG features. Finally, we feed the complete describers into the RF classifier for effective prediction. The experiments of 5-fold Cross-Validations (CV) yield mean accuracies of 88.96%, 86.37%, 82.88%, and 76.92% on four golden standard data sets (enzyme, ion channel, G protein-coupled receptors (GPCRs), and nuclear receptor, respectively). Moreover, the paper also conducts the state-of-art light gradient boosting machine (LGBM) and support vector machine (SVM) to further verify the performance of the proposed model. The experimental outcomes substantiate that the established model is feasible and reliable to predict DTIs. There is an excellent prospect that our model is capable of predicting DTIs as an efficient tool on a large scale.
APA, Harvard, Vancouver, ISO, and other styles
6

ZHANG, BAILING, and YIFAN ZHOU. "VEHICLE TYPE AND MAKE RECOGNITION BY COMBINED FEATURES AND ROTATION FOREST ENSEMBLE." International Journal of Pattern Recognition and Artificial Intelligence 26, no. 03 (May 2012): 1250004. http://dx.doi.org/10.1142/s0218001412500048.

Full text
Abstract:
Vehicle type/make recognition based on images captured by surveillance cameras is a challenging task in intelligent transportation system and automatic surveillance. In this paper, we comparatively studied two feature extraction methods for image description, i.e. a new multiresolution analysis tool called Fast Discrete Curvelet Transform and the pyramid histogram of oriented gradients (PHOG). Curvelet Transform has better directional and edge representation abilities than widely used wavelet transform, which is particularly appropriate for the description of images rich with edges. PHOG represents the local shape by a histogram of edge orientations computed for each image sub-region, quantized into a number of bins, thus has the ascendency in its description of more discriminating information. A composite feature description from PHOG and Curvelet can further increase the accuracy of classification by taking their complementary information. We also investigated the applicability of the Rotation Forest (RF) ensemble method for vehicle classification based on the combined features. The RF ensemble contains a set of base multilayer perceptrons which are trained using principal component analysis to rotate the original axes of combined features of vehicle images. The class label is assigned by the ensemble via majority voting. Experimental results using more than 600 images from 21 makes of cars/vans show the effectiveness of the proposed approach. The composite feature is better than any single feature in the classification accuracy and the ensemble model produces better performance compared to any of the individual neural network base classifier. With a moderate ensemble size of 20, the Rotation Forest ensembles offers a classification rate close to 96.5%, exhibiting promising potentials for real-life applications.
APA, Harvard, Vancouver, ISO, and other styles
7

Acharya, U. Rajendra, Yuki Hagiwara, Joel E. W. Koh, Jen Hong Tan, Sulatha V. Bhandary, A. Krishna Rao, and U. Raghavendra. "Automated screening tool for dry and wet age-related macular degeneration (ARMD) using pyramid of histogram of oriented gradients (PHOG) and nonlinear features." Journal of Computational Science 20 (May 2017): 41–51. http://dx.doi.org/10.1016/j.jocs.2017.03.005.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

ZHANG, BAILING. "RELIABLE IMAGE CLASSIFICATION BY COMBINING FEATURES AND RANDOM SUBSPACE SUPPORT VECTOR MACHINE ENSEMBLE." International Journal of Pattern Recognition and Artificial Intelligence 28, no. 03 (May 2014): 1450005. http://dx.doi.org/10.1142/s0218001414500050.

Full text
Abstract:
We investigate the implementation of image categorization algorithms with a reject option, as a mean to enhance the system reliability and to attain a higher classification accuracy. A reject option is desired in many image-classification applications for which the system should abstain from making decisions on the most uncertain images. Based on the random subspace (RS) ensemble learning model, a highly reliable image classification scheme is proposed by applying RS support vector machine (SVM) ensemble. Being different to previous classifier ensembles which focus on increasing classification accuracy exclusively, the objective of the proposed SVM ensemble is to provide classification confidence and implement reject option to accommodate the situations where no decision should be made. The ensemble is created with four different feature descriptions, including local binary pattern (LBP), pyramid histogram of oriented gradient (PHOG), Gabor filtering and curvelet transform. The consensus degree from the ensemble's voting conforms to the confidence measure and the rejection option is accomplished accordingly when the confidence falls below a threshold. The reliable recognition scheme is empirically evaluated on three image categorization benchmark databases, including the face database created by Aleix Martinez and Robert Benavente (AR faces), a subset of Caltech-101 images for object classification, and 15 natural scene categories, all of which yielded consistently high reliable results, thus demonstrating the effectiveness of the proposed approach. For example, a 99.9% accuracy was obtained with a rejection rate of 2.5% for the AR faces, which exhibit promising potentials for real-world applications.
APA, Harvard, Vancouver, ISO, and other styles
9

Saha, Soumyajit, Manosij Ghosh, Soulib Ghosh, Shibaprasad Sen, Pawan Kumar Singh, Zong Woo Geem, and Ram Sarkar. "Feature Selection for Facial Emotion Recognition Using Cosine Similarity-Based Harmony Search Algorithm." Applied Sciences 10, no. 8 (April 19, 2020): 2816. http://dx.doi.org/10.3390/app10082816.

Full text
Abstract:
Nowadays, researchers aim to enhance man-to-machine interactions by making advancements in several domains. Facial emotion recognition (FER) is one such domain in which researchers have made significant progresses. Features for FER can be extracted using several popular methods. However, there may be some redundant/irrelevant features in feature sets. In order to remove those redundant/irrelevant features that do not have any significant impact on classification process, we propose a feature selection (FS) technique called the supervised filter harmony search algorithm (SFHSA) based on cosine similarity and minimal-redundancy maximal-relevance (mRMR). Cosine similarity aims to remove similar features from feature vectors, whereas mRMR was used to determine the feasibility of the optimal feature subsets using Pearson’s correlation coefficient (PCC), which favors the features that have lower correlation values with other features—as well as higher correlation values with the facial expression classes. The algorithm was evaluated on two benchmark FER datasets, namely the Radboud faces database (RaFD) and the Japanese female facial expression (JAFFE). Five different state-of-the-art feature descriptors including uniform local binary pattern (uLBP), horizontal–vertical neighborhood local binary pattern (hvnLBP), Gabor filters, histogram of oriented gradients (HOG) and pyramidal HOG (PHOG) were considered for FS. Obtained results signify that our technique effectively optimized the feature vectors and made notable improvements in overall classification accuracy.
APA, Harvard, Vancouver, ISO, and other styles
10

Jia, Shi Jie, Yan Ping Yang, Jian Ying Zhao, and Nan Xiao. "Pyramid Histograms of Orientated Gradients for Product Image Retrieval." Advanced Materials Research 383-390 (November 2011): 5712–16. http://dx.doi.org/10.4028/www.scientific.net/amr.383-390.5712.

Full text
Abstract:
Traditional text-based image retrieval methods are hard to meet the requirements of on-line product search. This paper applied Content Based Image Retrieval (CBIR) technologies to e-commerce field and designed a product image retrieval algorithm based on Pyramid Histograms of Orientated Gradients (PHOG) descriptor and chi-square distance. By constructing the image retrieval system, we made retrieval tests on PI100 dataset from Microsoft Research Asia. The experimental results proved the efficiency of this algorithm.
APA, Harvard, Vancouver, ISO, and other styles
More sources

Dissertations / Theses on the topic "Pyramid histogram of oriented gradients (PHOG)"

1

Khan, Rizwan Ahmed. "Détection des émotions à partir de vidéos dans un environnement non contrôlé." Thesis, Lyon 1, 2013. http://www.theses.fr/2013LYO10227/document.

Full text
Abstract:
Dans notre communication quotidienne avec les autres, nous avons autant de considération pour l’interlocuteur lui-même que pour l’information transmise. En permanence coexistent en effet deux modes de transmission : le verbal et le non-verbal. Sur ce dernier thème intervient principalement l’expression faciale avec laquelle l’interlocuteur peut révéler d’autres émotions et intentions. Habituellement, un processus de reconnaissance d’émotions faciales repose sur 3 étapes : le suivi du visage, l’extraction de caractéristiques puis la classification de l’expression faciale. Pour obtenir un processus robuste apte à fournir des résultats fiables et exploitables, il est primordial d’extraire des caractéristiques avec de forts pouvoirs discriminants (selon les zones du visage concernées). Les avancées récentes de l’état de l’art ont conduit aujourd’hui à diverses approches souvent bridées par des temps de traitement trop couteux compte-tenu de l’extraction de descripteurs sur le visage complet ou sur des heuristiques mathématiques et/ou géométriques.En fait, aucune réponse bio-inspirée n’exploite la perception humaine dans cette tâche qu’elle opère pourtant régulièrement. Au cours de ces travaux de thèse, la base de notre approche fut ainsi de singer le modèle visuel pour focaliser le calcul de nos descripteurs sur les seules régions du visage essentielles pour la reconnaissance d’émotions. Cette approche nous a permis de concevoir un processus plus naturel basé sur ces seules régions émergentes au regard de la perception humaine. Ce manuscrit présente les différentes méthodologies bio-inspirées mises en place pour aboutir à des résultats qui améliorent généralement l’état de l’art sur les bases de référence. Ensuite, compte-tenu du fait qu’elles se focalisent sur les seules parties émergentes du visage, elles améliorent les temps de calcul et la complexité des algorithmes mis en jeu conduisant à une utilisation possible pour des applications temps réel
Communication in any form i.e. verbal or non-verbal is vital to complete various daily routine tasks and plays a significant role inlife. Facial expression is the most effective form of non-verbal communication and it provides a clue about emotional state, mindset and intention. Generally automatic facial expression recognition framework consists of three step: face tracking, feature extraction and expression classification. In order to built robust facial expression recognition framework that is capable of producing reliable results, it is necessary to extract features (from the appropriate facial regions) that have strong discriminative abilities. Recently different methods for automatic facial expression recognition have been proposed, but invariably they all are computationally expensive and spend computational time on whole face image or divides the facial image based on some mathematical or geometrical heuristic for features extraction. None of them take inspiration from the human visual system in completing the same task. In this research thesis we took inspiration from the human visual system in order to find from where (facial region) to extract features. We argue that the task of expression analysis and recognition could be done in more conducive manner, if only some regions are selected for further processing (i.e.salient regions) as it happens in human visual system. In this research thesis we have proposed different frameworks for automatic recognition of expressions, all getting inspiration from the human vision. Every subsequently proposed addresses the shortcomings of the previously proposed framework. Our proposed frameworks in general, achieve results that exceeds state-of-the-artmethods for expression recognition. Secondly, they are computationally efficient and simple as they process only perceptually salient region(s) of face for feature extraction. By processing only perceptually salient region(s) of the face, reduction in feature vector dimensionality and reduction in computational time for feature extraction is achieved. Thus making them suitable for real-time applications
APA, Harvard, Vancouver, ISO, and other styles

Conference papers on the topic "Pyramid histogram of oriented gradients (PHOG)"

1

Wang, Jin, Ping Liu, Mary F. H. She, Abbas Kouzani, and Saeid Nahavandi. "Human action recognition based on Pyramid Histogram of Oriented Gradients." In 2011 IEEE International Conference on Systems, Man and Cybernetics - SMC. IEEE, 2011. http://dx.doi.org/10.1109/icsmc.2011.6084045.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Tan, Zhi Rong, Shangxuan Tian, and Chew Lim Tan. "Using pyramid of histogram of oriented gradients on natural scene text recognition." In 2014 IEEE International Conference on Image Processing (ICIP). IEEE, 2014. http://dx.doi.org/10.1109/icip.2014.7025532.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Bhave, Sanket, Aniket Giri, Shravan Bhavsar, and Girija Chiddarwar. "Effective method for Shape based Image Retrieval using Pyramid of Histogram of Oriented Gradients." In 2019 International Conference on Computation of Power, Energy, Information and Communication (ICCPEIC). IEEE, 2019. http://dx.doi.org/10.1109/iccpeic45300.2019.9082407.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Thubsaeng, Wasin, Aram Kawewong, and Karn Patanukhom. "Vehicle logo detection using convolutional neural network and pyramid of histogram of oriented gradients." In 2014 11th International Joint Conference on Computer Science and Software Engineering (JCSSE). IEEE, 2014. http://dx.doi.org/10.1109/jcsse.2014.6841838.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Anakavej, Thitiphat, Aram Kawewong, and Karn Patanukhom. "Internet-Vision Based Vehicle Model Query System Using Eigenfaces and Pyramid of Histogram of Oriented Gradients." In 2013 International Conference on Signal-Image Technology & Internet-Based Systems (SITIS). IEEE, 2013. http://dx.doi.org/10.1109/sitis.2013.40.

Full text
APA, Harvard, Vancouver, ISO, and other styles
You might also be interested in the extended bibliographies on the topic 'Pyramid histogram of oriented gradients (PHOG)' for particular source types:
Journal articles
We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!

To the bibliography