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Relevant bibliographies by topics / Histogram intersection

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Academic literature on the topic 'Histogram intersection'

Author: Grafiati

Published: 7 June 2025

Last updated: 2 August 2025

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Journal articles on the topic "Histogram intersection"

1

Sun, Shengnan, Lindu Zhao, and Shicai Yang. "Gabor Weber Local Descriptor for Bovine Iris Recognition." Mathematical Problems in Engineering 2013 (2013): 1–7. http://dx.doi.org/10.1155/2013/920597.

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Iris recognition is a robust biometric technology. This paper proposes a novel local descriptor for bovine iris recognition, named Gabor Weber local descriptor (GWLD). We first compute the Gabor magnitude maps for the input bovine iris image, and then calculate the differential excitation and orientation for each pixel over each Gabor magnitude map. After that, we use these differential excitations and orientations to construct the GWLD histogram representation. Finally, histogram intersection is adopted to measure the similarity between different GWLD histograms. The experimental results on t

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Xi, Hanbin, and Tiantian Chang. "Image Classification Based on Histogram Intersection Kernel." Journal of Computer and Communications 03, no. 11 (2015): 158–63. http://dx.doi.org/10.4236/jcc.2015.311025.

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Lee, S. M., J. H. Xin, and S. Westland. "Evaluation of image similarity by histogram intersection." Color Research & Application 30, no. 4 (2005): 265–74. http://dx.doi.org/10.1002/col.20122.

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Wei, Guodong, Ying Tian, Shun’ichi Kaneko, and Zhengang Jiang. "Robust Template Matching Using Multiple-Layered Absent Color Indexing." Sensors 22, no. 17 (2022): 6661. http://dx.doi.org/10.3390/s22176661.

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Color is an essential feature in histogram-based matching. This can be extracted as statistical data during the comparison process. Although the applicability of color features in histogram-based techniques has been proven, position information is lacking during the matching process. We present a conceptually simple and effective method called multiple-layered absent color indexing (ABC-ML) for template matching. Apparent and absent color histograms are obtained from the original color histogram, where the absent colors belong to low-frequency or vacant bins. To determine the color range of co

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Hao, Peng-Yi, Yang Xia, Xiao-Xin Li, Sei-ichiro Kamata, and Sheng-Yong Chen. "Discriminative Histogram Intersection Metric Learning and Its Applications." Journal of Computer Science and Technology 32, no. 3 (2017): 507–19. http://dx.doi.org/10.1007/s11390-017-1740-0.

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Lee, Bong-Seok, and Young-Shik Moon. "Robust object tracking using projected motion and histogram intersection." KIPS Transactions:PartB 9B, no. 1 (2002): 99–104. http://dx.doi.org/10.3745/kipstb.2002.9b.1.099.

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Inoue, Kohei, Kenji Hara, and Kiichi Urahama. "Image and Video Clipping by Minimizing Weighted Histogram Intersection." Journal of The Institute of Image Information and Television Engineers 64, no. 11 (2010): 1639–46. http://dx.doi.org/10.3169/itej.64.1639.

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Okatev, Roman S., and Peter G. Frick. "Recurrence time statistics for chaos analysis in dynamical systems." Вестник Пермского университета. Физика, no. 3 (2024): 19–27. http://dx.doi.org/10.17072/1994-3598-2024-3-19-27.

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We propose a method of delimiting the existence domains of periodic, quasi-periodic and chaotic solutions of dynamical systems in the parameter space. The method is based on an analysis of the sampling of the times of passing a phase point from the previous intersection of the Poincaré plane to the next one (or from one local maximum to the next). An algorithm for generating a sample of recurrence times with subsequent analysis of the histogram of the obtained sample is constructed. A simple measure of histogram filling allows us to separate periodic and chaotic modes, as well as to estimate t

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Jia, Shi Jie, Jian Ying Zhao, Yan Ping Yang, and Nan Xiao. "Product-Image Classification with Support Vector Machine." Advanced Materials Research 433-440 (January 2012): 6019–22. http://dx.doi.org/10.4028/www.scientific.net/amr.433-440.6019.

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SVMs with kernel have been established with good generalization capabilities. This paper proposed a supervised product-image classification method based on SVM and Pyramid Histogram of words(PHOW). We tested several kernel functions on PI100 (Microsoft product-image dataset), such as linear, Radial Basis, Chi-square, histogram intersection and spatial pyramid kernel. Experimental results showed the effectiveness of our algorithm.

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Dhanushree, M., R. Priya, P. Aruna, and R. Bhavani. "A Framework for Video Summarization using Visual Attention Technique." Indian Journal Of Science And Technology 17, no. 15 (2024): 1586–95. http://dx.doi.org/10.17485/ijst/v17i15.456.

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Objectives: To develop an efficient Video Summarization technique that aims to utilize the saliency map for mimicking the human way of selecting the important events in the given video. Methods: This paper proposes Histogram based Weighted Fusion (HWF) algorithm that uses spatial and temporal saliency maps to act as guidance in creating the summary of the video. The spatial saliency score and temporal saliency score obtained from the corresponding saliency maps are fused using the proposed HWF algorithm to obtain the frame level importance score. It tries to depict the visual attention of the

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Book chapters on the topic "Histogram intersection"

1

Cheng, Jian, Siegbert Drüe, and Georg Hartmann. "Graph Based Histogram Intersection for Efficient Location of Color Objects." In Informatik aktuell. Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/978-3-642-59802-9_38.

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Freytag, Alexander, Erik Rodner, Paul Bodesheim, and Joachim Denzler. "Rapid Uncertainty Computation with Gaussian Processes and Histogram Intersection Kernels." In Computer Vision – ACCV 2012. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-37444-9_40.

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Zheng, Yong, Mayur Agnani, and Mili Singh. "Identification of Grey Sheep Users by Histogram Intersection in Recommender Systems." In Advanced Data Mining and Applications. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-69179-4_11.

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Müller, Wolfgang, and Andreas Henrich. "Faster Exact Histogram Intersection on Large Data Collections Using Inverted VA-Files." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-27814-6_54.

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Mei, Lin, Gerd Brunner, Lokesh Setia, and Hans Burkhardt. "Kernel Biased Discriminant Analysis Using Histogram Intersection Kernel for Content-Based Image Retrieval." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11508069_9.

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Bhattacharjee, Tanusree, Biplab Banerjee, and Nirmalya Chowdhury. "An Interactive Content Based Image Retrieval Method Integrating Intersection Kernel Based Support Vector Machine and Histogram Intersection Based Similarity Measure for Nearest Neighbor Ranking." In Information and Communication Technologies. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15766-0_74.

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Banerjee, Biplab, Tanusree Bhattacharjee, and Nirmalya Chowdhury. "Image Object Classification Using Scale Invariant Feature Transform Descriptor with Support Vector Machine Classifier with Histogram Intersection Kernel." In Information and Communication Technologies. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15766-0_71.

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Sasaki Shiori, Itabashi Yoshiko, Kiyoki Yasushi, and Chen Xing. "An Image-Query Creation Method for Representing Impression by Color-based Combination of Multiple Images." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2009. https://doi.org/10.3233/978-1-58603-957-8-105.

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This paper presents a dynamic image-query creation and metadata extraction method with semantic correlation computation between color-combinations and impressions of multiple image data. The main features of our method are (1) to create an image-query which reflects user's intention dynamically according to the color-based combinations of images with common features selected by a user as context, (2) to extract appropriate impression by each image collection which cannot be easily extracted from a single image, (3) to provide users an image retrieval environment reflecting historical and cultu

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Conference papers on the topic "Histogram intersection"

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Cheng, Erkang, Nianhua Xie, Haibin Ling, Predrag R. Bakic, Andrew D. A. Maidment, and Vasileios Megalooikonomou. "Mammographic image classification using histogram intersection." In 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro. IEEE, 2010. http://dx.doi.org/10.1109/isbi.2010.5490381.

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Boughorbel, S., J. P. Tarel, and N. Boujemaa. "Generalized histogram intersection kernel for image recognition." In rnational Conference on Image Processing. IEEE, 2005. http://dx.doi.org/10.1109/icip.2005.1530353.

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Gao, Xing, and Zhenjiang Miao. "Generalized Histogram Intersection kernel for image classification." In 2014 12th International Conference on Signal Processing (ICSP 2014). IEEE, 2014. http://dx.doi.org/10.1109/icosp.2014.7015127.

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Jiang Qiang-rong and Gao Yuan. "Face recognition based on Detail Histogram Intersection kernel." In 2009 IEEE International Conference on Intelligent Computing and Intelligent Systems (ICIS 2009). IEEE, 2009. http://dx.doi.org/10.1109/icicisys.2009.5357743.

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Wenjing Jia, Huaifeng Zhang, Xiangjian He, and Qiang Wu. "Gaussian Weighted Histogram Intersection for License Plate Classification." In 18th International Conference on Pattern Recognition (ICPR'06). IEEE, 2006. http://dx.doi.org/10.1109/icpr.2006.596.

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Barbu, Tudor, Mihaela Costin, and Adrian Ciobanu. "Histogram intersection based image retrieval technique using relevance feedback." In 2009 3rd International Workshop on Soft Computing Applications (SOFA). IEEE, 2009. http://dx.doi.org/10.1109/sofa.2009.5254878.

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Inoue, Kohei, Kenji Hara, and Kiichi Urahama. "Image and video clipping by weighted histogram intersection minimization." In 2010 IEEE Region 10 Conference (TENCON 2010). IEEE, 2010. http://dx.doi.org/10.1109/tencon.2010.5686033.

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Liu, Shu, Shao-Zi Li, Xian-Ming Liu, and Hong-Bo Zhang. "Entropy-based action features selection using histogram intersection kernel." In 2010 2nd International Conference on Signal Processing Systems (ICSPS). IEEE, 2010. http://dx.doi.org/10.1109/icsps.2010.5555433.

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Cheng, Y., S. Z. Li, and S. Z. Su. "Combine histogram intersection kernel with linear kernel for pedestrian classification." In IET International Conference on Information Science and Control Engineering 2012 (ICISCE 2012). Institution of Engineering and Technology, 2012. http://dx.doi.org/10.1049/cp.2012.2480.

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Tian Yi and Ruan Qiuqi. "Weight and Context Method for Action Recognition Using Histogram Intersection." In 5th IET International Conference on Wireless, Mobile and Multimedia Networks (ICWMMN 2013). Institution of Engineering and Technology, 2013. http://dx.doi.org/10.1049/cp.2013.2414.

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