Relevant bibliographies by topics / Spatial-Bag-of-Features

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Spatial-Bag-of-Features'

Author: Grafiati
Published: 4 June 2021
Last updated: 8 June 2024

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Journal articles on the topic "Spatial-Bag-of-Features"

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Feng, Jiangfan, Yuanyuan Liu, and Lin Wu. "Bag of Visual Words Model with Deep Spatial Features for Geographical Scene Classification." Computational Intelligence and Neuroscience 2017 (2017): 1–14. http://dx.doi.org/10.1155/2017/5169675.

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With the popular use of geotagging images, more and more research efforts have been placed on geographical scene classification. In geographical scene classification, valid spatial feature selection can significantly boost the final performance. Bag of visual words (BoVW) can do well in selecting feature in geographical scene classification; nevertheless, it works effectively only if the provided feature extractor is well-matched. In this paper, we use convolutional neural networks (CNNs) for optimizing proposed feature extractor, so that it can learn more suitable visual vocabularies from the
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Tao, Wenbing, Yicong Zhou, Liman Liu, Kunqian Li, Kun Sun, and Zhiguo Zhang. "Spatial adjacent bag of features with multiple superpixels for object segmentation and classification." Information Sciences 281 (October 2014): 373–85. http://dx.doi.org/10.1016/j.ins.2014.05.032.

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Mu, Guangyu, Ying Liu, and Limin Wang. "Considering the Spatial Layout Information of Bag of Features (BoF) Framework for Image Classification." PLOS ONE 10, no. 6 (2015): e0131164. http://dx.doi.org/10.1371/journal.pone.0131164.

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Tang, Yingjun, Li Xianhong, Zhu Wenqiang, Huang Shuying, and Zhang Yong. "Scene Classification Based on Spatial Semantic Topic." Journal of Computational and Theoretical Nanoscience 14, no. 1 (2017): 299–305. http://dx.doi.org/10.1166/jctn.2017.6320.

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We proposed a novel approach to provide BoV (Bag of Visterms) with spatial context in a low computational expense way, which includes three steps. At first, a pyramid was built to preserve the spatial context for features by fusing local features and global features, and adopted K-means was proposed to get codebook. Then common of general topics and uniqueness of category topics were fully considered on the middle layer to generate semantic topic representation for each image scene. At last, SVM (Support Vector Machine) was applied to do scene classification. We investigated our approach with
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Roy, Swalpa Kumar, Subhrasankar Chatterjee, Siddhartha Bhattacharyya, Bidyut B. Chaudhuri, and Jan Platos. "Lightweight Spectral–Spatial Squeeze-and- Excitation Residual Bag-of-Features Learning for Hyperspectral Classification." IEEE Transactions on Geoscience and Remote Sensing 58, no. 8 (2020): 5277–90. http://dx.doi.org/10.1109/tgrs.2019.2961681.

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Li, Xiaoman, Yanfei Zhong, Yu Su, and Richen Ye. "Scene-Change Detection Based on Multi-Feature-Fusion Latent Dirichlet Allocation Model for High-Spatial-Resolution Remote Sensing Imagery." Photogrammetric Engineering & Remote Sensing 87, no. 9 (2021): 669–81. http://dx.doi.org/10.14358/pers.20-00054.

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With the continuous development of high-spatial-resolution ground observation technology, it is now becoming possible to obtain more and more high-resolution images, which provide us with the possibility to understand remote sensing images at the semantic level. Compared with traditional pixel- and object-oriented methods of change detection, scene-change detection can provide us with land use change information at the semantic level, and can thus provide reliable information for urban land use change detection, urban planning, and government management. Most of the current scene-change detect
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Li, Zhen Wei, Jing Zhang, Xin Liu, and Li Zhuo. "Creating the Bag-of-Words with Spatial Context Information for Image Retrieval." Applied Mechanics and Materials 556-562 (May 2014): 4788–91. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.4788.

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Recently bag-of-words (BoW) model as image feature has been widely used in content-based image retrieval. Most of existing approaches of creating BoW ignore the spatial context information. In order to better describe the image content, the BoW with spatial context information is created in this paper. Firstly, image’s regions of interest are detected and the focus of attention shift is produced through visual attention model. The color and SIFT features are extracted from the region of interest and BoW is created through cluster analysis method. Secondly, the spatial context information among
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Wang, Yi, Wenke Yu, and Zhice Fang. "Multiple Kernel-Based SVM Classification of Hyperspectral Images by Combining Spectral, Spatial, and Semantic Information." Remote Sensing 12, no. 1 (2020): 120. http://dx.doi.org/10.3390/rs12010120.

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In this study, we present a hyperspectral image classification method by combining spectral, spatial, and semantic information. The main steps of the proposed method are summarized as follows: First, principal component analysis transform is conducted on an original image to produce its extended morphological profile, Gabor features, and superpixel-based segmentation map. To model spatial information, the extended morphological profile and Gabor features are used to represent structure and texture features, respectively. Moreover, the mean filtering is performed within each superpixel to maint
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Zhao, Bei, Yanfei Zhong, and Liangpei Zhang. "A spectral–structural bag-of-features scene classifier for very high spatial resolution remote sensing imagery." ISPRS Journal of Photogrammetry and Remote Sensing 116 (June 2016): 73–85. http://dx.doi.org/10.1016/j.isprsjprs.2016.03.004.

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Qiu, Weixing, and Zongxu Pan. "Polarimetric Synthetic Aperture Radar Ship Potential Area Extraction Based on Neighborhood Semantic Differences of the Latent Dirichlet Allocation Bag-of-Words Topic Model." Remote Sensing 15, no. 23 (2023): 5601. http://dx.doi.org/10.3390/rs15235601.

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Recently, deep learning methods have been widely studied in the field of polarimetric synthetic aperture radar (PolSAR) ship detection. However, extracting polarimetric and spatial features on the whole PolSAR image will result in high computational complexity. In addition, in the massive data ship detection task, the image to be detected contains a large number of invalid areas, such as land and seawater without ships. Therefore, using ship coarse detection methods to quickly locate the potential areas of ships, that is, ship potential area extraction, is an important prerequisite for PolSAR
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Dissertations / Theses on the topic "Spatial-Bag-of-Features"

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Alqasrawi, Yousef T. N. "Natural scene classification, annotation and retrieval : developing different approaches for semantic scene modelling based on Bag of Visual Words." Thesis, University of Bradford, 2012. http://hdl.handle.net/10454/5523.

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With the availability of inexpensive hardware and software, digital imaging has become an important medium of communication in our daily lives. A huge amount of digital images are being collected and become available through the internet and stored in various fields such as personal image collections, medical imaging, digital arts etc. Therefore, it is important to make sure that images are stored, searched and accessed in an efficient manner. The use of bag of visual words (BOW) model for modelling images based on local invariant features computed at interest point locations has become a stan
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K, C. Santosh. "Graphics Recognition using Spatial Relations and Shape Analysis." Thesis, Vandoeuvre-les-Nancy, INPL, 2011. http://www.theses.fr/2011INPL096N/document.

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Dans l’état de l’art actuel, la reconnaissance de symboles signifie généralement la reconnaissance des symboles isolés. Cependant, ces méthodes de reconnaissance de symboles isolés ne sont pas toujours adaptés pour résoudre les problèmes du monde réel. Dans le cas des documents composites qui contiennent des éléments textuels et graphiques, on doit être capable d’extraire et de formaliser les liens qui existent entre les images et le texte environnant, afin d’exploiter les informations incorporées dans ces documents.Liés à ce contexte, nous avons d’abord introduit une méthode de reconnaissance
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K, C. Santosh. "Graphics Recognition using Spatial Relations and Shape Analysis." Electronic Thesis or Diss., Vandoeuvre-les-Nancy, INPL, 2011. http://www.theses.fr/2011INPL096N.

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Dans l’état de l’art actuel, la reconnaissance de symboles signifie généralement la reconnaissance des symboles isolés. Cependant, ces méthodes de reconnaissance de symboles isolés ne sont pas toujours adaptés pour résoudre les problèmes du monde réel. Dans le cas des documents composites qui contiennent des éléments textuels et graphiques, on doit être capable d’extraire et de formaliser les liens qui existent entre les images et le texte environnant, afin d’exploiter les informations incorporées dans ces documents.Liés à ce contexte, nous avons d’abord introduit une méthode de reconnaissance
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Bhowmik, Neelanjan. "Recherche multi-descripteurs dans les fonds photographiques numérisés." Thesis, Paris Est, 2017. http://www.theses.fr/2017PESC1037/document.

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La recherche d’images par contenu (CBIR) est une discipline de l’informatique qui vise à structurer automatiquement les collections d’images selon des critères visuels. Les fonctionnalités proposées couvrent notamment l’accès efficace aux images dans une grande base de données d’images ou l’identification de leur contenu par des outils de détection et de reconnaissance d’objets. Ils ont un impact sur une large gamme de domaines qui manipulent ce genre de données, telles que le multimedia, la culture, la sécurité, la santé, la recherche scientifique, etc.Indexer une image à partir de son conten
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Book chapters on the topic "Spatial-Bag-of-Features"

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Wang, Chuanqian, Baochang Zhang, Zengchang Qin, and Junyi Xiong. "Spatial Weighting for Bag-of-Features Based Image Retrieval." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39515-4_8.

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Zheng, Yingbin, Hong Lu, Cheng Jin, and Xiangyang Xue. "Incorporating Spatial Correlogram into Bag-of-Features Model for Scene Categorization." In Computer Vision – ACCV 2009. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-12307-8_31.

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Passalis, Nikolaos, and Anastasios Tefas. "Spatial Bag of Features Learning for Large Scale Face Image Retrieval." In Advances in Big Data. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-47898-2_2.

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Thontadari C. and Prabhakar C. J. "Bag of Visual Words Based on Co-HOG Features for Word Spotting in Handwritten Documents." In Advancements in Computer Vision and Image Processing. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-5628-2.ch007.

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In this chapter, the authors present a segmentation-based word spotting method for handwritten documents using bag of visual words (BoVW) framework based on co-occurrence histograms of oriented gradients (Co-HOG) features. The Co-HOG descriptor captures the word image shape information and encodes the local spatial information by counting the co-occurrence of gradient orientation of neighbor pixel pairs. The handwritten document images are segmented into words and each word image is represented by a vector that contains the frequency of visual words appeared in the image. In order to include spatial information to the BoVW framework, the authors adopted spatial pyramid matching (SPM) method. The proposed method is evaluated using precision and recall metrics through experimentation conducted on popular datasets such as GW and IAM. The performance analysis confirmed that the method outperforms existing word spotting techniques.
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Conference papers on the topic "Spatial-Bag-of-Features"

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Cao, Yang, Changhu Wang, Zhiwei Li, Liqing Zhang, and Lei Zhang. "Spatial-bag-of-features." In 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2010. http://dx.doi.org/10.1109/cvpr.2010.5540021.

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Grzeszick, Rene, Leonard Rothacker, and Gernot A. Fink. "Bag-of-features representations using spatial visual vocabularies for object classification." In 2013 20th IEEE International Conference on Image Processing (ICIP). IEEE, 2013. http://dx.doi.org/10.1109/icip.2013.6738590.

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Shams, Mahsa Mirabdollahi, Hojat Kaveh, and Reza Safabakhsh. "Traffic sign recognition using an extended bag-of-features model with spatial histogram." In 2015 Signal Processing and Intelligent Systems Conference (SPIS). IEEE, 2015. http://dx.doi.org/10.1109/spis.2015.7422338.

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de Carvalho Soares, R., I. R. da Silva, and D. Guliato. "Spatial Locality Weighting of Features Using Saliency Map with a Bag-of-Visual-Words Approach." In 2012 IEEE 24th International Conference on Tools with Artificial Intelligence (ICTAI 2012). IEEE, 2012. http://dx.doi.org/10.1109/ictai.2012.151.

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Sosa-Garcia, Joan, and Francesca Odone. "Mean BoF per Quadrant - Simple and Effective Way to Embed Spatial Information in Bag of Features." In International Conference on Computer Vision Theory and Applications. SCITEPRESS - Science and and Technology Publications, 2015. http://dx.doi.org/10.5220/0005281002970304.

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Battiato, Sebastiano, Giovanni Maria Farinella, Enrico Messina, and Giovanni Puglisi. "A forensic signature based on spatial distributed bag of features for image alignment and tampering detection." In the 3rd international ACM workshop. ACM Press, 2011. http://dx.doi.org/10.1145/2072521.2072527.

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Wang, Y., W. F. Lu, J. Y. H. Fuh, and Y. S. Wong. "Bag-of-Features Sampling Techniques for 3D CAD Model Retrieval." In ASME 2011 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2011. http://dx.doi.org/10.1115/detc2011-48064.

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This paper investigates two sampling strategies, dense sampling and PHOW sampling, for bag-of-features 3D CAD model retrieval. Previous methods [1] use original salient SIFT feature detection for general 3D model retrieval which does not suit the need for CAD models representation. CAD models contain mostly piecewise-smooth surfaces and thus only sharp edges can be described. To overcome these limitations, two new sampling strategies are investigated to improve the feature extraction process. Dense sampling extracts SIFT features on regular spatial grids with even spacing. Pyramid Histogram Of
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Zhu, Qiqi, Yanfei Zhong, Bei Zhao, Guisong Xia, and Liangpei Zhang. "The bag-of-visual-words scene classifier combining local and global features for high spatial resolution imagery." In 2015 12th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD). IEEE, 2015. http://dx.doi.org/10.1109/fskd.2015.7382030.

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Chen, Jiale, Qiqi Zhu, Yanfei Zhong, Qingfeng Guan, Liangpei Zhang, and Deren Li. "Urban Scenes Change Detection Based on Multi-Scale Irregular Bag of Visual Features for High Spatial Resolution Imagery." In IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2020. http://dx.doi.org/10.1109/igarss39084.2020.9323144.

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