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Journal articles on the topic 'Histogram intersection'

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Published: 7 June 2025

Last updated: 17 July 2025

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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 the SEU bovine iris database verify the representation power of our proposed local descriptor.

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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 compared images, we propose a total color space (TCS) that can determine the operating range of the histogram bins. Furthermore, we invert the absent colors to obtain the properties of these colors using threshold hT. Then, we compute the similarity using the intersection. A multiple-layered structure is proposed against the shift issue in histogram-based approaches. Each layer is constructed using the isotonic principle. Thus, absent color indexing and multiple-layered structure are combined to solve the precision problem. Our experiments on real-world images and open data demonstrated that they have produced state-of-the-art results. Moreover, they retained the histogram merits of robustness in cases of deformation and scaling.

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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 the degree of chaoticity of intermediate modes. On simple model signals it is shown that the distribution of recurrence times gives information not contained in the spectral densities of the signal. Then, on the example of the classical Lorentz system, it is shown how a simple measure of filling the histogram of recurrence times allows us to obtain a visual map of modes. The paper presents the results of a comparative analysis of power spectral density and histograms of recurrence times for different modes realized in the Lorentz system at different values of the control parameter (Rayleigh number).

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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 human brain when watching a particular video. Findings: The experimental results show that the proposed HWF algorithm performs better than the state-of-the-art methods. Novelty: The use of Histogram intersection and the incorporation of the exponential function as the weight for the combined feature enhance the summarization ability of the proposed model. Keywords: Video Summarization, Saliency Map, Histogram intersection, Contrast sensitivity function, Attention curves

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Ragumadhavan, R., K. R. Aravind Britto, and R. Vimala. "Melanoma Skin Cancer Detection Using Wavelet Transform and Local Ternary Pattern." Journal of Medical Imaging and Health Informatics 12, no. 1 (2022): 15–19. http://dx.doi.org/10.1166/jmihi.2022.3856.

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Melanoma is the most serious form of skin cancer that affects millions of people globally. Through image analytics, early identification of skin cancer is enabled, resulting in more effective treatment and a lower mortality rate. The ph2 and human against machine datasets were used to collect images. After preprocessing the image with a weighted median filter, segmentation is investigated using a number of common techniques, with the best result generated by combining watershed transform and maximum similarity region merging. U-net architecture is explored for segmentation. Segmentation efficiency is calculated by dice loss and Jaccard coefficient. Segmentation architecture outperform the conventional method. Additionally, a novel wavelet transform-based approach is used to extract features, followed by local ternary pattern analysis. The intersection of the histograms, the Bhattacharya distance, the Chi-square distance, and the Pearson correlation coefficients are all computed. This inquiry makes use of only the Histogram intersection and Chi-square distance characteristics. Additional categorization is examined through the use of a range of machine learning algorithms, including the k-nearest neighbour approach, Bayesian classification, decision trees, and Support Vector Machines (SVM). When a Radial Basis Function (RBF) kernel based SVM is applied, the classification accuracy is maximised. This work is entirely devoted to binary categorization. As evidenced by the data, they outperform other state-of-the-art approaches reported in the literature. SVM classifies data with an accuracy of 98.6 percent. Weighted median filter, Watershed transform, Merging regions with the highest degree of similarity, Wavelet transform, Local Ternary Pattern, Histogram intersection Pearson correlation coefficient, chi-square distance Distance between Bhattacharya and support vector machine.

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

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Abstract 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 human brain when watching a particular video. Findings: The experimental results show that the proposed HWF algorithm performs better than the state-of-the-art methods. Novelty: The use of Histogram intersection and the incorporation of the exponential function as the weight for the combined feature enhance the summarization ability of the proposed model. Keywords: Video Summarization, Saliency Map, Histogram intersection, Contrast sensitivity function, Attention curves

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Sinha, Abhijeet Kumar, and K. K. Shukla. "A Study of Distance Metrics in Histogram Based Image Retrieval." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 4, no. 3 (2013): 821–30. http://dx.doi.org/10.24297/ijct.v4i3.4205.

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There has been a profound expansion of digital data both in terms of quality and heterogeneity. Trivial searching techniques of images by using metadata, keywords or tags are not sufficient. Efficient Content-based Image Retrieval (CBIR) is certainly the only solution to this problem. Difference between colors of two images can be an important metric to measure their similarity or dissimilarity. Content-based Image Retrieval is all about generating signatures of images in database and comparing the signature of the query image with these stored signatures. Color histogram can be used as signature of an image and used to compare two images based on certain distance metric.In this study, COREL Database is used for an exhaustive study of various distance metrics on different color spaces. Euclidean distance, Manhattan distance, Histogram Intersection and Vector Cosine Angle distances are used to compare histograms in both RGB and HSV color spaces. So, a total of 8 distance metrics for comparison of images for the sake of CBIR are discussed in this work.

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HU, GONGZHU, and ZE-NIAN LI. "AN X-CROSSING PRESERVING SKELETONIZATION ALGORITHM." International Journal of Pattern Recognition and Artificial Intelligence 07, no. 05 (1993): 1031–53. http://dx.doi.org/10.1142/s0218001493000522.

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For an image consisting of wire-like patterns, skeletonization (thinning) is often necessary as the first step towards feature extraction. But a serious problem which exists is that the intersections of the lines (X-crossings) will be elongated when applying a thinning algorithm to the image. That is, X-crossings are usually difficult to be preserved as the result of thinning. In this paper, we present a non-iterative line thinning method that preserves X-crossings of the lines in the image. The skeleton is formed by the mid-points of run-length encoding of the patterns. Line intersection areas are identified via a histogram analysis of the lengths of runs, and intersections are detected at locations where the sequences of runs merge or split.

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Baharom, Nur Amalina, Fatma Susilawati Mohamad, and Azizah Abdul Manaf. "Histogram-Based Detection of Tomato Maturity: A Preliminary Study." Malaysian Journal of Computing and Applied Mathematics 1, no. 2 (2018): 21–28. http://dx.doi.org/10.37231/myjcam.2018.1.2.21.

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Color is one of the most important features of images. Estimating the ripeness of fruits via color can be performed as it is the dominant feature in describing the information of image. However, each of color model has given different performance when used in experiment. This paper reports a preliminary study to identify the ripeness of tomato by extracting the color features and compute into Histogram based method. Histogram proposes the number of color intensities in ROI (Region of Interest) or a whole image to give more possibilities result. Histogram based techniques merely count the number or frequency of pixels in an image. The output value of color model will be used as an input to the classifier in future works. Similarity measures such as Euclidean distance, City Block, Manhattan distance or Histogram Intersection will be explored to calculate the image similarity rating.

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Becker, Dennis. "Analysis of the Histogram Intersection Kernel for Use in Bayesian Optimization." International Journal of Modeling and Optimization 7, no. 6 (2017): 337–45. http://dx.doi.org/10.7763/ijmo.2017.v7.609.

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Tian, Ying, Shun’ichi Kaneko, So Sasatani, Masaya Itoh, and Ming Fang. "Robust and Precise Matching Algorithm Combining Absent Color Indexing and Correlation Filter." Information 12, no. 10 (2021): 428. http://dx.doi.org/10.3390/info12100428.

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This paper presents a novel method that absorbs the strong discriminative ability from absent color indexing (ABC) to enhance sensitivity and combines it with a correlation filter (CF) for obtaining a higher precision; this method is named ABC-CF. First, by separating the original color histogram, apparent and absent colors are introduced. Subsequently, an automatic threshold acquisition is proposed using a mean color histogram. Next, a histogram intersection is selected to calculate the similarity. Finally, CF follows them to solve the drift caused by ABC during the matching process. The novel approach proposed in this paper realizes robustness in distortion of target images and higher margins in fundamental matching problems, and then achieves more precise matching in positions. The effectiveness of the proposed approach can be evaluated in the comparative experiments with other representative methods by use of the open data.

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Groce, Adam, Peter Rindal, and Mike Rosulek. "Cheaper Private Set Intersection via Differentially Private Leakage." Proceedings on Privacy Enhancing Technologies 2019, no. 3 (2019): 6–25. http://dx.doi.org/10.2478/popets-2019-0034.

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Abstract In this work we demonstrate that allowing differentially private leakage can significantly improve the concrete performance of secure 2-party computation (2PC) protocols. Specifically, we focus on the private set intersection (PSI) protocol of Rindal and Rosulek (CCS 2017), which is the fastest PSI protocol with security against malicious participants. We show that if differentially private leakage is allowed, the cost of the protocol can be reduced by up to 63%, depending on the desired level of differential privacy. On the technical side, we introduce a security model for differentially-private leakage in malicious-secure 2PC. We also introduce two new and improved mechanisms for “differentially private histogram overestimates,” the main technical challenge for differentially-private PSI.

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Chen, Haiyan, Ke Xie, Huan Wang, and Chunxia Zhao. "Scene image classification using locality-constrained linear coding based on histogram intersection." Multimedia Tools and Applications 77, no. 3 (2017): 4081–92. http://dx.doi.org/10.1007/s11042-017-4830-7.

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Yang, Wenji, Lingfu Kong, and Mingyan Wang. "Hand gesture recognition using saliency and histogram intersection kernel based sparse representation." Multimedia Tools and Applications 75, no. 10 (2015): 6021–34. http://dx.doi.org/10.1007/s11042-015-2947-0.

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Kusakunniran, Worapan, Anuwat Wiratsudakul, Udom Chuachan, et al. "Biometric for Cattle Identification using Muzzle Patterns." International Journal of Pattern Recognition and Artificial Intelligence 34, no. 12 (2020): 2056007. http://dx.doi.org/10.1142/s0218001420560078.

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Similar to human biometrics such as faces and fingerprints, animals also have biometrics for individual identifiers. This research paper works on biometrics of cattle using images of muzzle patterns. The proposed approach begins with a training process to construct a cattle face localization model using a Haar feature-based cascade classifier. Then, the watershed technique is applied to segment a region of interest (RoI) of a muzzle area in the detected region of the cattle face. This muzzle ROI is further enhanced to make ridge lines more outstanding. The next step, using two approaches, is to extract a main feature descriptor based on a bag of histograms of oriented gradients (BoHoG) and a histogram of local binary patterns (LBP). Then, the support vector machine (SVM) is applied with the histogram intersection kernel for a final cattle identifier. The proposed method is evaluated using five different datasets including one existing cattle dataset used in previous research works, one newly collected dataset of swamp buffalo captured in a controlled environment, and three newly collected datasets of swamp buffalo captured in an outdoor field environment. This outdoor field environment includes challenges of freely moving cattle and differences in daylight. It could achieve a promising accuracy of 95% for a large dataset of 431 subjects.

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Razmkhah, Mostafa, Fathimah Al-Ma'shumah, and Sohrab Effati. "The k-nearest Neighbor Classification of Histogram- and Trapezoid-Valued Data." Statistics, Optimization & Information Computing 10, no. 4 (2022): 1187–203. http://dx.doi.org/10.19139/soic-2310-5070-1451.

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‎A histogram-valued observation is a specific type of symbolic objects that represents its value by a list of bins (intervals) along with their corresponding relative frequencies or probabilities‎. ‎In the literature‎, ‎the raw data in bins of all histogram-valued data have been assumed to be uniformly distributed‎. ‎A new representation of such observations is proposed in this paper by assuming that the raw data in each bin are linearly distributed‎, ‎which are called trapezoid-valued data‎. ‎Moreover‎, ‎new definitions of union and intersection between trapezoid-valued observations are made‎. This study proposes the k-nearest neighbor technique for classifying histogram-valued data using various dissimilarity measures‎. ‎Further‎, ‎the limiting behavior of the computational complexities based on the performed dissimilarity measures are compared‎. ‎Some simulations are done to study the performance of the proposed procedures‎. ‎Also‎, ‎the results are applied to three various real data sets‎. ‎Eventually‎, ‎some conclusions are stated‎.

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Yu, Peng Fei, Hao Zhou, and Hai Yan Li. "Personal Identification Using Finger-Knuckle-Print Based on Local Binary Pattern." Applied Mechanics and Materials 441 (December 2013): 703–6. http://dx.doi.org/10.4028/www.scientific.net/amm.441.703.

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Over the last ten years, considerable progress has been made on the new hand-based biometric recognition, such as palmprint and hand vein. During this period, it has been proved that Finger-Knuckle-Print (FKP) can be used as a biometric identifier. In this paper, we present an effective FKP identification method based on Local Binary Pattern (LBP), whose idea is to divide the region of interest (ROI) of FKP into a set of sub-image blocks, which can be applied to extract the local features of the FKP. After that, LBP histograms of image blocks in a FKP ROI image are connected together to build the feature vector of the FKP ROI image. In the match stage, histogram intersection distance is applied as the similarity measurement between sample and template. Experimental results conducted on a database of 165 persons (4 fingers per person) show that the proposed method is effective.

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HIRAOKA, Toru, Singo MOMOSAKI, Tatsuro OCHI, and Kohei INOUE. "Land Cover Classification with Multispectral Image by Clustering Method Based on Histogram Intersection." Journal of the Japan society of photogrammetry and remote sensing 49, no. 5 (2010): 320–25. http://dx.doi.org/10.4287/jsprs.49.320.

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Devi, D., N. Kalaivani, and J. Anandhi. "A Novel Adaptive Pixel Retaining Approach for Robust Watermarking Using Histogram Intersection Technique." Journal of Computational and Theoretical Nanoscience 16, no. 2 (2019): 702–8. http://dx.doi.org/10.1166/jctn.2019.7794.

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Imperceptibility criterion as, the host image is intellectual property that the owners want to protect. It should be visually the increased usage of digitized data for communication leads to the high demand in the copyright protection of the data. This proposed watermarking technique helps to highly improve the capacity of embedding watermark without affecting the imperceptibility and robustness. The approach couples the idea of building analysis rule in generating the watermark, thereby increasing the capacity of embedding watermark. The approach is additionally guided with Histogram Intersection technique to improve the confidentiality of watermark. The APRA (Adaptive Pixel Retaining Approach) Algorithm helps to improve the imperceptibility of the extracted watermark. Watermark is embedded in host image by Singular Value Decomposition (SVD). The APRA Algorithm helps to rebuild the extracted watermark in such a way that it remains close to the original watermark. With the available dataset, the experiments are performed and obtained results shows that the proposed watermarking technique yields watermarked images with high imperceptibility and robustness to common attacks.

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Li, Pan, Yang Liu, Guojun Liu, Maozu Guo, and Zhiyong Pan. "A robust local sparse coding method for image classification with Histogram Intersection Kernel." Neurocomputing 184 (April 2016): 36–42. http://dx.doi.org/10.1016/j.neucom.2015.07.136.

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Bicciato, Alessandro, Luca Cosmo, Giorgia Minello, Luca Rossi, and Andrea Torsello. "GNN-LoFI: A novel graph neural network through localized feature-based histogram intersection." Pattern Recognition 148 (April 2024): 110210. http://dx.doi.org/10.1016/j.patcog.2023.110210.

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Qu, Zhong, and Teng Fei Gao. "An Improved Algorithm of Keyframe Extraction for Video Summarization." Advanced Materials Research 225-226 (April 2011): 807–11. http://dx.doi.org/10.4028/www.scientific.net/amr.225-226.807.

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Video segmentation and keyframe extraction are the basis of Content-based Video Retrieval (CBVR), in which keyframe selection plays the central role in CBVR. In this paper, as the initialization of keyframe extraction, we proposed an improved approach of key-frame extraction for video summarization. In our approach, videos were firstly segmented into shots according to video content, by our improved histogram-based method, with the use of histogram intersection and nonuniform partitioning and weighting. Then, within each shot, keyframes were determined with the calculation of image entropy as a reflection of the quantity of image information in HSV color space of every frame. Our simulation results in section 4 prove that extracted key frames with our method are compact and faithful to the original video.

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Park, Sohee, Hansung Lee, Jang-Hee Yoo, Geonwoo Kim, and Soonja Kim. "Partially Occluded Facial Image Retrieval Based on a Similarity Measurement." Mathematical Problems in Engineering 2015 (2015): 1–11. http://dx.doi.org/10.1155/2015/217568.

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We present a partially occluded facial image retrieval method based on a similarity measurement for forensic applications. The main novelty of this method compared with other occluded face recognition algorithms is measuring the similarity based on Scale Invariant Feature Transform (SIFT) matching between normal gallery images and occluded probe images. The proposed method consists of four steps: (i) a Self-Quotient Image (SQI) is applied to input images, (ii) Gabor-Local Binary Pattern (Gabor-LBP) histogram features are extracted from the SQI images, (iii) the similarity between two compared images is measured by using the SIFT matching algorithm, and (iv) histogram intersection is performed on the SIFT-based similarity measurement. In experiments, we have successfully evaluated the performance of the proposed method with the commonly used benchmark database, including occluded facial images. The results show that the correct retrieval ratio was 94.07% in sunglasses occlusion and 93.33% in scarf occlusion. As such, the proposed method achieved better performance than other Gabor-LBP histogram-based face recognition algorithms in eyes-hidden occlusion of facial images.

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NISHIMURA, Koki, Shunji MAEDA, Tomoyuki ARAKI, et al. "Proposal of Extraction Method of Inherent Striated Toolmarks for Bullet Discrimination using Histogram Intersection." Journal of the Japan Society for Precision Engineering 83, no. 8 (2017): 781–88. http://dx.doi.org/10.2493/jjspe.83.781.

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Rodner, Erik, Alexander Freytag, Paul Bodesheim, Björn Fröhlich, and Joachim Denzler. "Large-Scale Gaussian Process Inference with Generalized Histogram Intersection Kernels for Visual Recognition Tasks." International Journal of Computer Vision 121, no. 2 (2016): 253–80. http://dx.doi.org/10.1007/s11263-016-0929-y.

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Dong, Lumei, Gongping Yang, Yilong Yin, Xiaoming Xi, Lu Yang, and Fei Liu. "Finger Vein Verification with Vein Textons." International Journal of Pattern Recognition and Artificial Intelligence 29, no. 04 (2015): 1556003. http://dx.doi.org/10.1142/s0218001415560030.

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Finger vein pattern has become one of the most promising biometric identifiers. In this paper, a robust method based on Bag-of-Words (BoW) is developed for finger vein verification. Firstly, some robust and discriminative visual words are learned from local base features such as Local Binary Pattern (LBP), Mean Curvature and Webber Local Descriptor (WLD). We name these visual words as Finger Vein Textons (FVTs). Secondly, each image is mapped into a FVTs matrix. Finally, spatial pyramid matching (SPM) method is applied to maintain spatial layout information by representing each image as pyramid histogram which is performed for matching by histogram intersection function. Experimental results show that the proposed method achieves satisfactory performance both on our database and the open PolyU database. In addition, our method also has strong robustness and high accuracy on the self-built rotation and illumination databases.

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Shi, Ying, Yuan Wan, Xinjian Wang, and Huanhuan Li. "Incorporation of Histogram Intersection and Semantic Information into Non-Negative Local Laplacian Sparse Coding for Image Classification." Mathematics 13, no. 2 (2025): 219. https://doi.org/10.3390/math13020219.

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Traditional sparse coding has proven to be an effective method for image feature representation in recent years, yielding promising results in image classification. However, it faces several challenges, such as sensitivity to feature variations, code instability, and inadequate distance measures. Additionally, image representation and classification often operate independently, potentially resulting in the loss of semantic relationships. To address these issues, a new method is proposed, called Histogram intersection and Semantic information-based Non-negativity Local Laplacian Sparse Coding (HS-NLLSC) for image classification. This method integrates Non-negativity and Locality into Laplacian Sparse Coding (NLLSC) optimisation, enhancing coding stability and ensuring that similar features are encoded into similar codewords. In addition, histogram intersection is introduced to redefine the distance between feature vectors and codebooks, effectively preserving their similarity. By comprehensively considering both the processes of image representation and classification, more semantic information is retained, thereby leading to a more effective image representation. Finally, a multi-class linear Support Vector Machine (SVM) is employed for image classification. Experimental results on four standard and three maritime image datasets demonstrate superior performance compared to the previous six algorithms. Specifically, the classification accuracy of our approach improved by 5% to 19% compared to the previous six methods. This research provides valuable insights for various stakeholders in selecting the most suitable method for specific circumstances.

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FUJII, Shu, Shunji MAEDA, Tomoyuki ARAKI, et al. "Proposal of Histogram Intersection with Weighting and Location-Preservation of Striated Toolmarks for Bullet Discrimination." Journal of the Japan Society for Precision Engineering 82, no. 7 (2016): 672–78. http://dx.doi.org/10.2493/jjspe.82.672.

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Maharani, Devira Anggi, Carmadi Machbub, and Lenni Yulianti. "Real-time Human Tracking System using Histogram Intersection Distance in Firefly Optimization Based Particle Filter." International Journal on Electrical Engineering and Informatics 13, no. 4 (2021): 853–72. http://dx.doi.org/10.15676/ijeei.2021.13.4.7.

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Kim, Chang-Ryong, and Chin-Wan Chung. "A multi-step approach for partial similarity search in large image data using histogram intersection." Information and Software Technology 45, no. 4 (2003): 203–15. http://dx.doi.org/10.1016/s0950-5849(02)00206-9.

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Li, Yong, Xiang Shi, Hongbin Jin, and Zhigang Wen. "Detecting lines and building intersection correspondences by computing edge oriented histogram on multi-sensor images." Infrared Physics & Technology 73 (November 2015): 1–7. http://dx.doi.org/10.1016/j.infrared.2015.08.013.

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Bilal, Muhammad. "Algorithmic optimisation of histogram intersection kernel support vector machine‐based pedestrian detection using low complexity features." IET Computer Vision 11, no. 5 (2017): 350–57. http://dx.doi.org/10.1049/iet-cvi.2016.0403.

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Su, Song-Zhi, and Shu-Yuan Chen. "Analysis of Feature Fusion Based on HIK SVM and Its Application for Pedestrian Detection." Abstract and Applied Analysis 2013 (2013): 1–11. http://dx.doi.org/10.1155/2013/436062.

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This work presents the fusion of integral channel features to improve the effectiveness and efficiency of pedestrian detection. The proposed method combines the histogram of oriented gradient (HOG) and local binary pattern (LBP) features by a concatenated fusion method. Although neural network (NN) is an efficient tool for classification, the time complexity is heavy. Hence, we choose support vector machine (SVM) with the histogram intersection kernel (HIK) as a classifier. On the other hand, although many datasets have been collected for pedestrian detection, few are designed to detect pedestrians in low-resolution visual images and at night time. This work collects two new pedestrian datasets—one for low-resolution visual images and one for near-infrared images—to evaluate detection performance on various image types and at different times. The proposed fusion method uses only images from the INRIA dataset for training but works on the two newly collected datasets, thereby avoiding the training overhead for cross-datasets. The experimental results verify that the proposed method has high detection accuracies even in the variations of image types and time slots.

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Jiang, Qiang Rong, and Zhe Wu. "Face Recognition Based on Maximum Spanning Tree Kernel and Gray Kernel." Applied Mechanics and Materials 513-517 (February 2014): 1387–91. http://dx.doi.org/10.4028/www.scientific.net/amm.513-517.1387.

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Face Recognition has been recognized as a major research field in pattern recognition and computer vision. This technique is widely adopted, because of its unique convenience, economy and high accuracy compared to other biological recognition techniques. An interesting and important challenge is thus to investigate high-efficient recognition algorithm. The introduction of kernel methods in pattern recognition has been received significant attentions in the recent several years, and gray kernel and graph kernel are two popular approaches. The paper proposes maximum spanning tree kernel and region histogram intersection kernel; moreover, experiments demonstrate that higher face recognition accuracy can be achieved by multiple kernels which are the combination of them.

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Munoz-Minjares, Jorge, Osbaldo Vite-Chavez, Jorge Flores-Troncoso, and Jorge M. Cruz-Duarte. "Alternative Thresholding Technique for Image Segmentation Based on Cuckoo Search and Generalized Gaussians." Mathematics 9, no. 18 (2021): 2287. http://dx.doi.org/10.3390/math9182287.

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Object segmentation is a widely studied topic in digital image processing, as to it can be used for countless applications in several fields. This process is traditionally achieved by computing an optimal threshold from the image intensity histogram. Several algorithms have been proposed to find this threshold based on different statistical principles. However, the results generated via these algorithms contradict one another due to the many variables that can disturb an image. An accepted strategy to achieve the optimal histogram threshold, to distinguish between the object and the background, is to estimate two data distributions and find their intersection. This work proposes a strategy based on the Cuckoo Search Algorithm (CSA) and the Generalized Gaussian (GG) distribution to assess the optimal threshold. To test this methodology, we carried out several experiments in synthetic and practical scenarios and compared our results against other well-known algorithms from the literature. These practical cases comprise a medical image database and our own generated database. The results in a simulated environment show an evident advantage of the proposed strategy against other algorithms. In a real environment, this ranks among the best algorithms, making it a reliable alternative.

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Esmaeili, Vida, Mahmood Mohassel Feghhi, and Seyed Omid Shahdi. "Micro-Expression Recognition based on the Multi-Color ULBP and Histogram of Gradient Direction from Six Intersection Planes." Journal of Iranian Association of Electrical and Electronics Engineers 19, no. 3 (2022): 123–30. http://dx.doi.org/10.52547/jiaeee.19.3.123.

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Huang, Xian Ying, and Wei Wei Chen. "An Effective Image Matching Algorithm Based on Rotation Independence." Applied Mechanics and Materials 347-350 (August 2013): 3685–90. http://dx.doi.org/10.4028/www.scientific.net/amm.347-350.3685.

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Traditional image matching algorithms has poor accuracy in image comparing, such as histogram intersection method. A new image matching algorithm based on the similarity comparison of irregular shape is presented in this paper, which divides the image into a number of irregular regions according to different colors, and extracts the boundary points of the irregular region to compose an irregular shape. The direction and distance is used to comparing the two irregular shapes if the rotation of the image is not considered, otherwise circular list is used to ignore the image rotation. It can be used widely. If two irregular shapes are similar, the two images are considered similar. Experiment proves that this method can effectively improve the image matching accuracy.

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Gulbin, Yuri. "ON ESTIMATION AND HYPOTHESIS TESTING OF THE GRAIN SIZE DISTRIBUTION BY THE SALTYKOV METHOD." Image Analysis & Stereology 27, no. 3 (2011): 163. http://dx.doi.org/10.5566/ias.v27.p163-174.

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The paper considers the problem of validity of unfolding the grain size distribution with the back-substitution method. Due to the ill-conditioned nature of unfolding matrices, it is necessary to evaluate the accuracy and precision of parameter estimation and to verify the possibility of expected grain size distribution testing on the basis of intersection size histogram data. In order to review these questions, the computer modeling was used to compare size distributions obtained stereologically with those possessed by three-dimensional model aggregates of grains with a specified shape and random size. Results of simulations are reported and ways of improving the conventional stereological techniques are suggested. It is shown that new improvements in estimating and testing procedures enable grain size distributions to be unfolded more efficiently.

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Markert, Kel N., Amanda M. Markert, Timothy Mayer, et al. "Comparing Sentinel-1 Surface Water Mapping Algorithms and Radiometric Terrain Correction Processing in Southeast Asia Utilizing Google Earth Engine." Remote Sensing 12, no. 15 (2020): 2469. http://dx.doi.org/10.3390/rs12152469.

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Satellite remote sensing plays an important role in the monitoring of surface water for historical analysis and near real-time applications. Due to its cloud penetrating capability, many studies have focused on providing efficient and high quality methods for surface water mapping using Synthetic Aperture Radar (SAR). However, few studies have explored the effects of SAR pre-processing steps used and the subsequent results as inputs into surface water mapping algorithms. This study leverages the Google Earth Engine to compare two unsupervised histogram-based thresholding surface water mapping algorithms utilizing two distinct pre-processed Sentinel-1 SAR datasets, specifically one with and one without terrain correction. The resulting surface water maps from the four different collections were validated with user-interpreted samples from high-resolution Planet Scope data. It was found that the overall accuracy from the four collections ranged from 92% to 95% with Cohen’s Kappa coefficients ranging from 0.7999 to 0.8427. The thresholding algorithm that samples a histogram based on water edge information performed best with a maximum accuracy of 95%. While the accuracies varied between methods it was found that there is no statistical significant difference between the errors of the different collections. Furthermore, the surface water maps generated from the terrain corrected data resulted in a intersection over union metrics of 95.8%–96.4%, showing greater spatial agreement, as compared to 92.3%–93.1% intersection over union using the non-terrain corrected data. Overall, it was found that algorithms using terrain correction yield higher overall accuracy and yielded a greater spatial agreement between methods. However, differences between the approaches presented in this paper were not found to be significant suggesting both methods are valid for generating accurate surface water maps. High accuracy surface water maps are critical to disaster planning and response efforts, thus results from this study can help inform SAR data users on the pre-processing steps needed and its effects as inputs on algorithms for surface water mapping applications.

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Parajuli, Sujan, Rajesh Khadka, and Deepak Kunwar. "Modelling of Traffic Noise Pollution at Signalized Intersection: A Case Study of Baneshwor Intersection." International Journal of Traffic Management in Transportation Network 1, no. 1 (2025): 1–14. https://doi.org/10.46610/ijtmtn.2025.v01i01.001.

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The growth of motor vehicles and human activities in major cities has led to noise pollution problems due to traffic movement and other motorized activities in these confined areas. Road traffic noise decreases human efficiency and productivity, and also lowers the quality of living standard. The people residing near such regions are worried about the environmental effects of traffic noise and its impact on human health. Assessing traffic-generated noise problems and their effects requires planning actions, such as traffic noise modeling, to develop a preventive and mitigatory plan that provides a framework for reducing noise levels. The study was conducted which presents the status and modelling of noise pollution at signalized intersections in New Baneshwor, Kathmandu, Nepal. Noise level data collection was conducted through a sound level meter (Lutron Model: SL-4023SD) whereas simultaneously classified traffic volume, composition, and speed data was collected using a videographic survey. A video camera was used to record the movement of traffic vehicles to determine traffic volume, traffic speed, and traffic composition during peak hours. The sound meter was used simultaneously with a video camera to measure the sound level at that time interval. Simultaneous measurement of the video graphics survey and sound level survey was done with the help of the time setting function in the sound meter with that clock time exactly as seen in the video graphics survey. At each station, the location was marked and noted through GPS devices on smartphones, and the sound level meter (SLM) was used for sound measurement at that station. For the modeling of noise induced by road traffic, statistical analysis was carried out using SPSS software whereas Arc GIS was used for mapping urban road noise. The findings show that noise levels at the selected intersection where we had carried out our study, exceeded the allowable noise level limit which was set by the Ministry of Environment Science and Technology, Government of Nepal. A statistical model was developed based on multiple linear regression (MLR) analysis, which was carried out between traffic noise as the dependent variable and traffic volume, traffic composition, and traffic speed as independent variables that help to predict noise levels. In this study, for the determination of road traffic noise, A-weighted equivalent continuous sound level (LAeq) is used as the dependent variable, while the independent variables include the percentage of heavy vehicles, traffic volume, and speed. The paired t-test was employed to assess the goodness-of-fit of the developed model. The Durbin-Watson test was conducted to check for the independence of the statistical data observations. An ANOVA test was used to examine homoscedasticity, and the results were found to be satisfactory. A histogram with an overlaid normal curve was created to verify whether the residuals of the variables followed a normal distribution. Also, the modeled noise level was compared with measured noise levels obtained from the sound level meter and the validity of the prediction model was confirmed using statistical tools. The noise levels induced by traffic were positively correlated with the volume of moving vehicles and composition of mixed traffic, i.e., the presence of heavier vehicles, whereas it was negatively correlated with the speed of vehicle. With the rise in traffic volume, there was an increase in noise level. The distribution of traffic has also an impact on the noise level produced on roads. Motorcycles have dominated the traffic composition, which become a key factor in influencing the traffic noise level. At intersections, the speed of vehicles was found to be negatively correlated with traffic noise level due to having a lower speed value rather than at free road sections due to congestion. At low speeds, the impact of vehicles on noise measurement was greater, and noise level was found to be higher.

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Günay, Asuman, and Vasif V. Nabiyev. "Facial Age Estimation Using Spatial Weber Local Descriptor." International Journal of Advances in Telecommunications, Electrotechnics, Signals and Systems 6, no. 3 (2017): 108. http://dx.doi.org/10.11601/ijates.v6i3.218.

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This paper introduces a novel age estimation method using a new texture descriptor Weber Local Descriptor (WLD). This texture descriptor is analyzed in depth for age estimation problem. In the study, the multi-scale versions of holistic and spatial WLD (SWLD) descriptors are used to extract the age related features from normalized facial images. After finding a lower dimensional feature subspace, age estimation is performed using multiple linear regression. In addition the age estimation accuracy of each of the distinct and intersection block used in spatial texture extraction are investigated. Experiments on FGNET, MORPH and PAL databases have shown that similar age estimation performances can be obtained by using more effective blocks in spatial histogram generation. This also provides us to reduce the number of features and computational cost.

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Ren, Yaoyao, Xing Li, Fangyuqing Jin, et al. "Extracting Regular Building Footprints Using Projection Histogram Method from UAV-Based 3D Models." ISPRS International Journal of Geo-Information 14, no. 1 (2024): 6. https://doi.org/10.3390/ijgi14010006.

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Extracting building outlines from 3D models poses significant challenges stemming from the intricate diversity of structures and the complexity of urban scenes. Current techniques heavily rely on human expertise and involve repetitive, labor-intensive manual operations. To address these limitations, this paper presents an innovative automatic technique for accurately extracting building footprints, particularly those with gable and hip roofs, directly from 3D data. Our methodology encompasses several key steps: firstly, we construct a triangulated irregular network (TIN) to capture the intricate geometry of the buildings. Subsequently, we employ 2D indexing and counting grids for efficient data processing and utilize a sophisticated connected component labeling algorithm to precisely identify the extents of the roofs. A single seed point is manually specified to initiate the process, from which we select the triangular facets representing the outer walls of the buildings. Utilizing the projection histogram method, these facets are grouped and processed to extract regular building footprints. Extensive experiments conducted on datasets from Nanjing and Wuhan demonstrate the remarkable accuracy of our approach. With mean intersection over union (mIOU) values of 99.2% and 99.4%, respectively, and F1 scores of 94.3% and 96.7%, our method proves to be both effective and robust in mapping building footprints from 3D real-scene data. This work represents a significant advancement in automating the extraction of building footprints from complex 3D scenes, with potential applications in urban planning, disaster response, and environmental monitoring.

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Subramaniam, Umashankar, M. Monica Subashini, Dhafer Almakhles, Alagar Karthick, and S. Manoharan. "An Expert System for COVID-19 Infection Tracking in Lungs Using Image Processing and Deep Learning Techniques." BioMed Research International 2021 (November 13, 2021): 1–17. http://dx.doi.org/10.1155/2021/1896762.

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The proposed method introduces algorithms for the preprocessing of normal, COVID-19, and pneumonia X-ray lung images which promote the accuracy of classification when compared with raw (unprocessed) X-ray lung images. Preprocessing of an image improves the quality of an image increasing the intersection over union scores in segmentation of lungs from the X-ray images. The authors have implemented an efficient preprocessing and classification technique for respiratory disease detection. In this proposed method, the histogram of oriented gradients (HOG) algorithm, Haar transform (Haar), and local binary pattern (LBP) algorithm were applied on lung X-ray images to extract the best features and segment the left lung and right lung. The segmentation of lungs from the X-ray can improve the accuracy of results in COVID-19 detection algorithms or any machine/deep learning techniques. The segmented lungs are validated over intersection over union scores to compare the algorithms. The preprocessed X-ray image results in better accuracy in classification for all three classes (normal/COVID-19/pneumonia) than unprocessed raw images. VGGNet, AlexNet, Resnet, and the proposed deep neural network were implemented for the classification of respiratory diseases. Among these architectures, the proposed deep neural network outperformed the other models with better classification accuracy.

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Lemenkova, Polina. "Distance-based vegetation indices computed by SAGA GIS: A comparison of the perpendicular and transformed soil adjusted approaches for the LANDSAT TM image." Poljoprivredna tehnika 46, no. 3 (2021): 49–60. http://dx.doi.org/10.5937/poljteh2103049l.

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Landsat-TM of 2001 covering Iceland (15.5°W-21°W, 64.5°N-67°N) was processed using SAGA GIS for testing distance-based Vegetation Indices (VIs): four approaches of Perpendicular Vegetation Index (PVI) and two approaches of Transformed Soil Adjusted Vegetation Index TSAVI. The PVI of vegetation from the soil background line indicated healthiness as a leaf area index (LAI). The results showed that the reflectance for vegetation has a linear relation with soil background line. Four PVI models and two TSAVI shown coefficients of determination with LAI. The dataset demonstrate variations in the calculated coefficients. The mode in the histograms of the PVI based on four different algorithms show the difference:-7.1,-8.36, 2.78 and 7.0. The dataset for the two approaches of TSAVI: first case ranges in 4.4.-80.6 with a bell-shape mode of a histogram (8.09 to 23.29) for the first algorithm and an irregular shape for the second algorithm with several modes starting from 0.11 to 0.2 and decreasing to 0.26. SAGA GIS permits the calculation of PVI and TSAVI by computed NDVI based on the intersection of vegetation and soil background. Masking the NIR and R, a linear regression of grids was performed using an equation embedded in SAGA GIS. The advantages of the distance-based PVI and TSAVI consists in the adjusted position of pixels on the soil brightness line which refines it comparing to the slope-based VIs. The paper demonstrates SAGA GIS application in agricultural studies.

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