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An Overview of Bag of Words;Importance, Implementation, Applications, and Challenges
International Enformatika Conference, 2019In the past fifteen years, the grow of using Bag of Words (BoW) method in the field of computer vision is visibly observed. In addition,-for the text classification and texture recognition, it can also be used in classification of images, videos, robot ...
Wisam Qader, Musa M. Ameen, Bilal Ahmed
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Bag-of-Words Modelling for Speech Recognition
2009 International Conference on Future Computer and Communication, 2009A semantic language modelling method for speech recognition is presented. The method is somehow similar to latent semantic analysis, but it does not need so much memory and training data. Even though it gave better experimental results, provided as percentage of correctly recognized sentences from a corpus.
Bartosz Ziolko +2 more
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Distinguish Polarity in Bag-of-Words Visualization
Proceedings of the AAAI Conference on Artificial Intelligence, 2017Neural network-based BOW models reveal that word-embedding vectors encode strong semantic regularities. However, such models are insensitive to word polarity. We show that, coupled with simple information such as word spellings, word-embedding vectors can preserve both semantic regularity and conceptual polarity without supervision. We
Yusheng Xie +3 more
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Accelerating Bag-of-Words with SOM
2019We propose a fast Bag-of-Words (BoW) method for image classification, inspired by the mechanism that arrangement of neurons in visual cortex can preserve the topology of mapping from inputs, and the fact that human brain can retrieve information almost instantly.
Jian-Hui Chen +2 more
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Two improved continuous bag-of-word models
2017 International Joint Conference on Neural Networks (IJCNN), 2017Data representation is a fundamental task in machine learning, which affects the performance of the whole machine learning system. In the past few years, with the rapid development of deep learning, the models for word embedding based on neural networks have brought new inspiration to the research of natural language processing.
Qi Wang, Jungang Xu, Hong Chen, Ben He
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Image Representation with Bag-of-Words
2016Image classification, which is to assign one or more category labels to an image, is a very hot topic in computer vision and pattern recognition. It can be applied in video surveillance, remote sensing, web content analysis, biometrics, etc. Many successful models transform low-level descriptors into richer mid-level representations.
Xiang Xu, Xingkun Wu, Feng Lin
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BRINGING ORDER IN THE BAG OF WORDS
Proceedings of the International Conference on Computer Vision Theory and Applications, 2012International audience ; This paper presents a method to infuse spatial information in the bag of words (BOW) framework for object categorization. The main idea is to account the local spatial distribution of the visual words. Rather than finding rigid local patterns, we consider the visual words in close spatial proximity as a pouch of words and we ...
Zhang, Shihong +3 more
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Graph-based bag-of-words for classification
Pattern Recognition, 2018Abstract This paper introduces the Bag of Graphs (BoG), a Bag-of-Words model that encodes in graphs the local structures of a digital object. We present a formal definition, introducing concepts and rules that make this model flexible and adaptable for different applications. We define two BoG-based methods – Bag of Singleton Graphs (BoSG) and Bag of
Fernanda B. Silva +4 more
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Bag-of-words representations for computer audition
2022Maschinelles Hören ist im täglichen Leben allgegenwärtig, mit Anwendungen, die von personalisierten virtuellen Agenten bis hin zum Gesundheitswesen reichen. Aus technischer Sicht besteht das Ziel darin, den Inhalt eines Audiosignals hinsichtlich einer Auswahl definierter Labels robust zu klassifizieren. Die Labels beschreiben bspw.
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Contextual Bag-of-Words for Visual Categorization
IEEE Transactions on Circuits and Systems for Video Technology, 2011Bag-of-words (BOW), which represents an image by the histogram of local patches on the basis of a visual vocabulary, has attracted intensive attention in visual categorization due to its good performance and flexibility. Conventional BOW neglects the contextual relations between local patches due to its Naive Bayesian assumption.
Li, T Li, Teng +3 more
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