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Academic literature on the topic 'Bag of visual words model'

Author: Grafiati
Published: 4 June 2021
Last updated: 14 February 2022

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Journal articles on the topic "Bag of visual words model"

1

Zhang, Shilin, and Xunyuan Zhang. "Pedestrian Density Estimation by a Weighted Bag of Visual Words Model." International Journal of Machine Learning and Computing 5, no. 3 (2015): 214–18. http://dx.doi.org/10.7763/ijmlc.2015.v5.509.

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Zurita, Baldemar, Luís Luna, José Hernández, and José Ramírez. "Hybrid Classification in Bag of Visual Words Model." Circulation in Computer Science 3, no. 4 (2018): 10–15. http://dx.doi.org/10.22632/ccs-2018-252-85.

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Classification of images by means of the BOVW method is well known and applied in different recognition projects, this method rely on three phases: detection and extraction of characteristics, representation of the image and finally the classification. SIFT, Kmeans and SVM is the most accepted combination. This article aims to demonstrate that this combination is not always the best choice for all types of datasets, different training sets of images were created from scratch and will be used for the bag of visual words model: the first phase of detection and extraction, SIFT will be used, later in the second phase a dictionary of words will be created through a clustering process using K-means, EM, K-means in combination with EM, finally, for classification it will be compared the algorithms of SVM, Gaussian NB, KNN, Decision Tree, Random Forest, Neural Network and AdaBoost in order to determine the performance and accuracy of every method.
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Jang, Hyunwoong, and Soosun Cho. "Image Classification Using Bag of Visual Words and Visual Saliency Model." KIPS Transactions on Software and Data Engineering 3, no. 12 (2014): 547–52. http://dx.doi.org/10.3745/ktsde.2014.3.12.547.

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4

Qi, Yali, Guoshan Zhang, and Yeli Li. "Image Classification Model Using Visual Bag of Semantic Words." Pattern Recognition and Image Analysis 29, no. 3 (2019): 404–14. http://dx.doi.org/10.1134/s1054661819030222.

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5

N. Sultani, Zainab, and Ban N. Dhannoon. "Modified Bag of Visual Words Model for Image Classification." Al-Nahrain Journal of Science 24, no. 2 (2021): 78–86. http://dx.doi.org/10.22401/anjs.24.2.11.

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Image classification is acknowledged as one of the most critical and challenging tasks in computer vision. The bag of visual words (BoVW) model has proven to be very efficient for image classification tasks since it can effectively represent distinctive image features in vector space. In this paper, BoVW using Scale-Invariant Feature Transform (SIFT) and Oriented Fast and Rotated BRIEF(ORB) descriptors are adapted for image classification. We propose a novel image classification system using image local feature information obtained from both SIFT and ORB local feature descriptors. As a result, the constructed SO-BoVW model presents highly discriminative features, enhancing the classification performance. Experiments on Caltech-101 and flowers dataset prove the effectiveness of the proposed method.
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Manzo, Mario, and Simone Pellino. "Bag of ARSRG Words (BoAW)." Machine Learning and Knowledge Extraction 1, no. 3 (2019): 871–82. http://dx.doi.org/10.3390/make1030050.

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In recent years researchers have worked to understand image contents in computer vision. In particular, the bag of visual words (BoVW) model, which describes images in terms of a frequency histogram of visual words, is the most adopted paradigm. The main drawback is the lack of information about location and the relationships between features. For this purpose, we propose a new paradigm called bag of ARSRG (attributed relational SIFT (scale-invariant feature transform) regions graph) words (BoAW). A digital image is described as a vector in terms of a frequency histogram of graphs. Adopting a set of steps, the images are mapped into a vector space passing through a graph transformation. BoAW is evaluated in an image classification context on standard datasets and its effectiveness is demonstrated through experimental results compared with well-known competitors.
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Xu, Ye, Xiaodong Yu, Tian Wang, and Zezhong Xu. "Pooling region learning of visual word for image classification using bag-of-visual-words model." PLOS ONE 15, no. 6 (2020): e0234144. http://dx.doi.org/10.1371/journal.pone.0234144.

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Li, Fang-fang, Si-wei Luo, Xi-yao Liu, and Bei-ji Zou. "Bag-of-visual-words model for artificial pornographic images recognition." Journal of Central South University 23, no. 6 (2016): 1383–89. http://dx.doi.org/10.1007/s11771-016-3190-1.

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9

Budiarta, Komang, Dewa Made Wiharta, and Komang Oka Saputra. "Balinese Mask Characters Classification using Bag of Visual Words Model." Journal of Electrical, Electronics and Informatics 5, no. 1 (2021): 25. http://dx.doi.org/10.24843/jeei.2021.v05.i01.p05.

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Mask, often known by Balinese as “Tapel”, is made of pule wood. It depicts the representation of characters in the “badbad” or legend. Bali has many types of mask dances that are often performed, which makes tourists interested in visiting Bali. Unfortunately, many tourists do not know the information contained in Balinese masks. The most important information contained in the character of the Balinese masks. The characters of each mask are different even though they have the same type. As mask art is also a cultural heritage from generation to generation, it needs to be preserved. It is necessary to have information in the form of technology that can distinguish the characters from Balinese masks. In this study, bag of visual word method in the classification process of Balinese mask characters is used, where in this method, there are several algorithms used, namely SURF as feature detection, K-Means as a clustering process to get the value of feature quantization, and SVM as a classification of Balinese mask character. The result of the accuracy level obtained from the testing process is 80%.
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10

Nazir, Saima, Muhammad Haroon Yousaf, Jean-Christophe Nebel, and Sergio A. Velastin. "Dynamic Spatio-Temporal Bag of Expressions (D-STBoE) Model for Human Action Recognition." Sensors 19, no. 12 (2019): 2790. http://dx.doi.org/10.3390/s19122790.

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Human action recognition (HAR) has emerged as a core research domain for video understanding and analysis, thus attracting many researchers. Although significant results have been achieved in simple scenarios, HAR is still a challenging task due to issues associated with view independence, occlusion and inter-class variation observed in realistic scenarios. In previous research efforts, the classical bag of visual words approach along with its variations has been widely used. In this paper, we propose a Dynamic Spatio-Temporal Bag of Expressions (D-STBoE) model for human action recognition without compromising the strengths of the classical bag of visual words approach. Expressions are formed based on the density of a spatio-temporal cube of a visual word. To handle inter-class variation, we use class-specific visual word representation for visual expression generation. In contrast to the Bag of Expressions (BoE) model, the formation of visual expressions is based on the density of spatio-temporal cubes built around each visual word, as constructing neighborhoods with a fixed number of neighbors could include non-relevant information making a visual expression less discriminative in scenarios with occlusion and changing viewpoints. Thus, the proposed approach makes the model more robust to occlusion and changing viewpoint challenges present in realistic scenarios. Furthermore, we train a multi-class Support Vector Machine (SVM) for classifying bag of expressions into action classes. Comprehensive experiments on four publicly available datasets: KTH, UCF Sports, UCF11 and UCF50 show that the proposed model outperforms existing state-of-the-art human action recognition methods in term of accuracy to 99.21%, 98.60%, 96.94 and 94.10%, respectively.
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