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Journal articles on the topic 'Local visual feature'

Author: Grafiati

Published: 10 March 2023

Last updated: 31 July 2025

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1

Jia, Xi Bin, and Mei Xia Zheng. "Video Based Visual Speech Feature Model Construction." Applied Mechanics and Materials 182-183 (June 2012): 1367–71. http://dx.doi.org/10.4028/www.scientific.net/amm.182-183.1367.

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This paper aims to give a solutions for the construction of chinese visual speech feature model based on HMM. We propose and discuss three kind representation model of the visual speech which are lip geometrical features, lip motion features and lip texture features. The model combines the advantages of the local LBP and global DCT texture information together, which shows better performance than the single feature. Equally the model combines the advantages of the local LBP and geometrical information together is better than single feature. By computing the recognition rate of the visemes from

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Wang, Yin-Tien, Chen-Tung Chi, and Ying-Chieh Feng. "Robot mapping using local invariant feature detectors." Engineering Computations 31, no. 2 (2014): 297–316. http://dx.doi.org/10.1108/ec-01-2013-0024.

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Purpose – To build a persistent map with visual landmarks is one of the most important steps for implementing the visual simultaneous localization and mapping (SLAM). The corner detector is a common method utilized to detect visual landmarks for constructing a map of the environment. However, due to the scale-variant characteristic of corner detection, extensive computational cost is needed to recover the scale and orientation of corner features in SLAM tasks. The purpose of this paper is to build the map using a local invariant feature detector, namely speeded-up robust features (SURF), to de

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Huang, Cuiyang, and Zihan Hu. "A multimodal transformer-based visual question answering method integrating local and global information." PLOS One 20, no. 7 (2025): e0324757. https://doi.org/10.1371/journal.pone.0324757.

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Addressing the limitations in current visual question answering (VQA) models face limitations in multimodal feature fusion capabilities and often lack adequate consideration of local information, this study proposes a multimodal Transformer VQA network based on local and global information integration (LGMTNet). LGMTNet employs attention on local features within the context of global features, enabling it to capture both broad and detailed image information simultaneously, constructing a deep encoder-decoder module that directs image feature attention based on the question context, thereby enh

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Zhang, Yuanpeng, Jingye Guan, Haobo Wang, Kaiming Li, Ying Luo, and Qun Zhang. "Generalized Zero-Shot Space Target Recognition Based on Global-Local Visual Feature Embedding Network." Remote Sensing 15, no. 21 (2023): 5156. http://dx.doi.org/10.3390/rs15215156.

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Existing deep learning-based space target recognition methods rely on abundantly labeled samples and are not capable of recognizing samples from unseen classes without training. In this article, based on generalized zero-shot learning (GZSL), we propose a space target recognition framework to simultaneously recognize space targets from both seen and unseen classes. First, we defined semantic attributes to describe the characteristics of different categories of space targets. Second, we constructed a dual-branch neural network, termed the global-local visual feature embedding network (GLVFENet)

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Sun, Huadong, Xu Zhang, Xiaowei Han, Xuesong Jin, and Zhijie Zhao. "Commodity Image Classification Based on Improved Bag-of-Visual-Words Model." Complexity 2021 (March 17, 2021): 1–10. http://dx.doi.org/10.1155/2021/5556899.

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With the increasing scale of e-commerce, the complexity of image content makes commodity image classification face great challenges. Image feature extraction often determines the quality of the final classification results. At present, the image feature extraction part mainly includes the underlying visual feature and the intermediate semantic feature. The intermediate semantics of the image acts as a bridge between the underlying features and the advanced semantics of the image, which can make up for the semantic gap to a certain extent and has strong robustness. As a typical intermediate sem

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Dong, Baoyu, and Guang Ren. "A New Scene Classification Method Based on Local Gabor Features." Mathematical Problems in Engineering 2015 (2015): 1–14. http://dx.doi.org/10.1155/2015/109718.

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A new scene classification method is proposed based on the combination of local Gabor features with a spatial pyramid matching model. First, new local Gabor feature descriptors are extracted from dense sampling patches of scene images. These local feature descriptors are embedded into a bag-of-visual-words (BOVW) model, which is combined with a spatial pyramid matching framework. The new local Gabor feature descriptors have sufficient discrimination abilities for dense regions of scene images. Then the efficient feature vectors of scene images can be obtained byK-means clustering method and vi

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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,

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Gao, Yuhang, and Long Zhao. "Coarse TRVO: A Robust Visual Odometry with Detector-Free Local Feature." Journal of Advanced Computational Intelligence and Intelligent Informatics 26, no. 5 (2022): 731–39. http://dx.doi.org/10.20965/jaciii.2022.p0731.

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The visual SLAM system requires precise localization. To obtain consistent feature matching results, visual features acquired by neural networks are being increasingly used to replace traditional manual features in situations with weak texture, motion blur, or repeated patterns. However, to improve the level of accuracy, most deep learning enhanced SLAM systems, which have a decreased efficiency. In this paper, we propose Coarse TRVO, a visual odometry system that uses deep learning for feature matching. The deep learning network uses a CNN and transformer structures to provide dense high-qual

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Aw, Y. K., Robyn Owens, and John Ross. "An analysis of local energy and phase congruency models in visual feature detection." Journal of the Australian Mathematical Society. Series B. Applied Mathematics 40, no. 1 (1998): 97–122. http://dx.doi.org/10.1017/s0334270000012406.

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AbstractA variety of approaches have been developed for the detection of features such as edges, lines, and corners in images. Many techniques presuppose the feature type, such as a step edge, and use the differential properties of the luminance function to detect the location of such features. The local energy model provides an alternative approach, detecting a variety of feature types in a single pass by analysing order in the phase components of the Fourier transform of the image. The local energy model is usually implemented by calculating the envelope of the analytic signal associated wit

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Manandhar, Dipu, Kim-Hui Yap, Zhenwei Miao, and Lap-Pui Chau. "Lattice-Support repetitive local feature detection for visual search." Pattern Recognition Letters 98 (October 2017): 123–29. http://dx.doi.org/10.1016/j.patrec.2017.09.021.

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Yang, Hong-Ying, Yong-Wei Li, Wei-Yi Li, Xiang-Yang Wang, and Fang-Yu Yang. "Content-based image retrieval using local visual attention feature." Journal of Visual Communication and Image Representation 25, no. 6 (2014): 1308–23. http://dx.doi.org/10.1016/j.jvcir.2014.05.003.

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Xiang, Wenhao, Jianjun Shen, Li Zhang, and Yu Zhang. "Infrared and Visual Image Fusion Based on a Local-Extrema-Driven Image Filter." Sensors 24, no. 7 (2024): 2271. http://dx.doi.org/10.3390/s24072271.

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The objective of infrared and visual image fusion is to amalgamate the salient and complementary features of the infrared and visual images into a singular informative image. To accomplish this, we introduce a novel local-extrema-driven image filter designed to effectively smooth images by reconstructing pixel intensities based on their local extrema. This filter is iteratively applied to the input infrared and visual images, extracting multiple scales of bright and dark feature maps from the differences between continuously filtered images. Subsequently, the bright and dark feature maps of th

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Wang, Jian, Yueming Song, Ce Song, Haonan Tian, Shuai Zhang, and Jinghui Sun. "CVTrack: Combined Convolutional Neural Network and Vision Transformer Fusion Model for Visual Tracking." Sensors 24, no. 1 (2024): 274. http://dx.doi.org/10.3390/s24010274.

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Most single-object trackers currently employ either a convolutional neural network (CNN) or a vision transformer as the backbone for object tracking. In CNNs, convolutional operations excel at extracting local features but struggle to capture global representations. On the other hand, vision transformers utilize cascaded self-attention modules to capture long-range feature dependencies but may overlook local feature details. To address these limitations, we propose a target-tracking algorithm called CVTrack, which leverages a parallel dual-branch backbone network combining CNN and Transformer

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Han, Xian-Hua, and Yen-Wei Chen. "Biomedical Imaging Modality Classification Using Combined Visual Features and Textual Terms." International Journal of Biomedical Imaging 2011 (2011): 1–7. http://dx.doi.org/10.1155/2011/241396.

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We describe an approach for the automatic modality classification in medical image retrieval task of the 2010 CLEF cross-language image retrieval campaign (ImageCLEF). This paper is focused on the process of feature extraction from medical images and fuses the different extracted visual features and textual feature for modality classification. To extract visual features from the images, we used histogram descriptor of edge, gray, or color intensity and block-based variation as global features and SIFT histogram as local feature. For textual feature of image representation, the binary histogram

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Liu, Chang, Zhuocheng Zou, Yuan Miao, and Jun Qiu. "Light field quality assessment based on aggregation learning of multiple visual features." Optics Express 30, no. 21 (2022): 38298. http://dx.doi.org/10.1364/oe.467754.

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Light field imaging is a way to represent human vision from a computational perspective. It contains more visual information than traditional imaging systems. As a basic problem of light field imaging, light field quality assessment has received extensive attention in recent years. In this study, we explore the characteristics of light field data for different visual domains (spatial, angular, coupled, projection, and depth), study the multiple visual features of a light field, and propose a non-reference light field quality assessment method based on aggregation learning of multiple visual fe

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Wang, Di, Hongying Zhang, and Yanhua Shao. "A Robust Invariant Local Feature Matching Method for Changing Scenes." Wireless Communications and Mobile Computing 2021 (December 28, 2021): 1–13. http://dx.doi.org/10.1155/2021/8927822.

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The precise evaluation of camera position and orientation is a momentous procedure of most machine vision tasks, especially visual localization. Aiming at the shortcomings of local features of dealing with changing scenes and the problem of realizing a robust end-to-end network that worked from feature detection to matching, an invariant local feature matching method for changing scene image pairs is proposed, which is a network that integrates feature detection, descriptor constitution, and feature matching. In the feature point detection and descriptor construction stage, joint training is c

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Fawad, Muhammad Jamil Khan, and MuhibUr Rahman. "Person Re-Identification by Discriminative Local Features of Overlapping Stripes." Symmetry 12, no. 4 (2020): 647. http://dx.doi.org/10.3390/sym12040647.

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The human visual system can recognize a person based on his physical appearance, even if extreme spatio-temporal variations exist. However, the surveillance system deployed so far fails to re-identify the individual when it travels through the non-overlapping camera’s field-of-view. Person re-identification (Re-ID) is the task of associating individuals across disjoint camera views. In this paper, we propose a robust feature extraction model named Discriminative Local Features of Overlapping Stripes (DLFOS) that can associate corresponding actual individuals in the disjoint visual surveillance

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Liu, Xianglong, Bo Lang, Yi Xu, and Bo Cheng. "Feature grouping and local soft match for mobile visual search." Pattern Recognition Letters 33, no. 3 (2012): 239–46. http://dx.doi.org/10.1016/j.patrec.2011.10.002.

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He, Xuan, Wang Gao, Chuanzhen Sheng, et al. "LiDAR-Visual-Inertial Odometry Based on Optimized Visual Point-Line Features." Remote Sensing 14, no. 3 (2022): 622. http://dx.doi.org/10.3390/rs14030622.

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This study presents a LiDAR-Visual-Inertial Odometry (LVIO) based on optimized visual point-line features, which can effectively compensate for the limitations of a single sensor in real-time localization and mapping. Firstly, an improved line feature extraction in scale space and constraint matching strategy, using the least square method, is proposed to provide a richer visual feature for the front-end of LVIO. Secondly, multi-frame LiDAR point clouds were projected into the visual frame for feature depth correlation. Thirdly, the initial estimation results of Visual-Inertial Odometry (VIO)

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Du, Siliang, Yilin Xiao, Jingwei Huang, Mingwei Sun, and Mingzhong Liu. "Guided Local Feature Matching with Transformer." Remote Sensing 15, no. 16 (2023): 3989. http://dx.doi.org/10.3390/rs15163989.

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GLFNet is proposed to be utilized for the detection and matching of local features among remote-sensing images, with existing sparse feature points being leveraged as guided points. Local feature matching is a crucial step in remote-sensing applications and 3D reconstruction. However, existing methods that detect feature points in image pairs and match them separately may fail to establish correct matches among images with significant differences in lighting or perspectives. To address this issue, the problem is reformulated as the extraction of corresponding features in the target image, give

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Zhang, Peng, and Wenfen Liu. "DLALoc: Deep-Learning Accelerated Visual Localization Based on Mesh Representation." Applied Sciences 13, no. 2 (2023): 1076. http://dx.doi.org/10.3390/app13021076.

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Visual localization, i.e., the camera pose localization within a known three-dimensional (3D) model, is a basic component for numerous applications such as autonomous driving cars and augmented reality systems. The most widely used methods from the literature are based on local feature matching between a query image that needs to be localized and database images with known camera poses and local features. However, this method still struggles with different illumination conditions and seasonal changes. Additionally, the scene is normally presented by a sparse structure-from-motion point cloud t

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Zhang, Jun, Zhicheng Zhao, and Xilan Tian. "ISAR Image Quality Assessment Based on Visual Attention Model." Applied Sciences 15, no. 4 (2025): 1996. https://doi.org/10.3390/app15041996.

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The quality of ISAR (Inverse Synthetic Aperture Radar) images has a significant impact on the detection and recognition of targets. Therefore, ISAR image quality assessment is a fundamental prerequisite and primary link in the utilization of ISAR images. Previous ISAR image quality assessment methods typically extract hand-crafted features or use simple multi-layer networks to extract local features. Hand-crafted features and local features from networks usually lack the global information of ISAR images. Furthermore, most deep neural networks obtain feature representations by abridging the pr

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Arshad, Saba, and Tae-Hyoung Park. "SVS-VPR: A Semantic Visual and Spatial Information-Based Hierarchical Visual Place Recognition for Autonomous Navigation in Challenging Environmental Conditions." Sensors 24, no. 3 (2024): 906. http://dx.doi.org/10.3390/s24030906.

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Robust visual place recognition (VPR) enables mobile robots to identify previously visited locations. For this purpose, the extracted visual information and place matching method plays a significant role. In this paper, we critically review the existing VPR methods and group them into three major categories based on visual information used, i.e., handcrafted features, deep features, and semantics. Focusing the benefits of convolutional neural networks (CNNs) and semantics, and limitations of existing research, we propose a robust appearance-based place recognition method, termed SVS-VPR, which

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Chi, Jianning, Xiaosheng Yu, Yifei Zhang, and Huan Wang. "A Novel Local Human Visual Perceptual Texture Description with Key Feature Selection for Texture Classification." Mathematical Problems in Engineering 2019 (February 4, 2019): 1–20. http://dx.doi.org/10.1155/2019/3756048.

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This paper proposes a novel local texture description method which defines six human visual perceptual characteristics and selects the minimal subset of relevant as well as nonredundant features based on principal component analysis (PCA). We assign six texture characteristics, which were originally defined by Tamura et al., with novel definition and local metrics so that these measurements reflect the human perception of each characteristic more precisely. Then, we propose a PCA-based feature selection method exploiting the structure of the principal components of the feature set to find a su

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Ying, Shanquan, Jianfeng Zhao, Guannan Li, and Junjie Dai. "LIM: Lightweight Image Local Feature Matching." Journal of Imaging 11, no. 5 (2025): 164. https://doi.org/10.3390/jimaging11050164.

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Image matching is a fundamental problem in computer vision, serving as a core component in tasks such as visual localization, structure from motion, and SLAM. While recent advances using convolutional neural networks and transformer have achieved impressive accuracy, their substantial computational demands hinder practical deployment on resource-constrained devices, such as mobile and embedded platforms. To address this challenge, we propose LIM, a lightweight image local feature matching network designed for computationally constrained embedded systems. LIM integrates efficient feature extrac

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Awad, Ali Ismail, and M. Hassaballah. "Bag-of-Visual-Words for Cattle Identification from Muzzle Print Images." Applied Sciences 9, no. 22 (2019): 4914. http://dx.doi.org/10.3390/app9224914.

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Cattle, buffalo and cow identification plays an influential role in cattle traceability from birth to slaughter, understanding disease trajectories and large-scale cattle ownership management. Muzzle print images are considered discriminating cattle biometric identifiers for biometric-based cattle identification and traceability. This paper presents an exploration of the performance of the bag-of-visual-words (BoVW) approach in cattle identification using local invariant features extracted from a database of muzzle print images. Two local invariant feature detectors—namely, speeded-up robust f

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Sarwar, Amna, Zahid Mehmood, Tanzila Saba, Khurram Ashfaq Qazi, Ahmed Adnan, and Habibullah Jamal. "A novel method for content-based image retrieval to improve the effectiveness of the bag-of-words model using a support vector machine." Journal of Information Science 45, no. 1 (2018): 117–35. http://dx.doi.org/10.1177/0165551518782825.

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The advancements in the multimedia technologies result in the growth of the image databases. To retrieve images from such image databases using visual attributes of the images is a challenging task due to the close visual appearance among the visual attributes of these images, which also introduces the issue of the semantic gap. In this article, we recommend a novel method established on the bag-of-words (BoW) model, which perform visual words integration of the local intensity order pattern (LIOP) feature and local binary pattern variance (LBPV) feature to reduce the issue of the semantic gap

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Norhisham Razali, Mohd, Noridayu Manshor, Alfian Abdul Halin, Norwati Mustapha, and Razali Yaakob. "Fuzzy encoding with hybrid pooling for visual dictionary in food recognition." Indonesian Journal of Electrical Engineering and Computer Science 21, no. 1 (2021): 179. http://dx.doi.org/10.11591/ijeecs.v21.i1.pp179-195.

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<span>Tremendous number of f food images in the social media services can be exploited by using food recognition for healthcare benefits and food industry marketing. The main challenges in food recognition are the large variability of food appearance that often generates a highly diverse and ambiguous descriptions of local feature. Ironically, the ambiguous descriptions of local feature have triggered information loss in visual dictionary constructions from the hard assignment practices. The current method based on hard assignment and Fisher vector approach to construct visual dictionary

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Razali, Mohd Norhisham, Noridayu Manshor, Alfian Abdul Halin, Norwati Mustapha, and Razali Yaakob. "Fuzzy encoding with hybrid pooling for visual dictionary in food recognition." Indonesian Journal of Electrical Engineering and Computer Science 21, no. 1 (2021): 179–95. https://doi.org/10.11591/ijeecs.v21.i1.pp179-195.

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Tremendous number of f food images in the social media services can be exploited by using food recognition for healthcare benefits and food industry marketing. The main challenges in food recognition are the large variability of food appearance that often generates a highly diverse and ambiguous descriptions of local feature. Ironically, the ambiguous descriptions of local feature have triggered information loss in visual dictionary constructions from the hard assignment practices. The current method based on hard assignment and Fisher vector approach to construct visual dictionary have unexpe

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Wu, Hui, Min Wang, Wengang Zhou, Yang Hu, and Houqiang Li. "Learning Token-Based Representation for Image Retrieval." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 3 (2022): 2703–11. http://dx.doi.org/10.1609/aaai.v36i3.20173.

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In image retrieval, deep local features learned in a data-driven manner have been demonstrated effective to improve retrieval performance. To realize efficient retrieval on large image database, some approaches quantize deep local features with a large codebook and match images with aggregated match kernel. However, the complexity of these approaches is non-trivial with large memory footprint, which limits their capability to jointly perform feature learning and aggregation. To generate compact global representations while maintaining regional matching capability, we propose a unified framewor

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Zhang, Yachao, Runze Hu, Ronghui Li, Yanyun Qu, Yuan Xie, and Xiu Li. "Cross-Modal Match for Language Conditioned 3D Object Grounding." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 7 (2024): 7359–67. http://dx.doi.org/10.1609/aaai.v38i7.28566.

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Language conditioned 3D object grounding aims to find the object within the 3D scene mentioned by natural language descriptions, which mainly depends on the matching between visual and natural language. Considerable improvement in grounding performance is achieved by improving the multimodal fusion mechanism or bridging the gap between detection and matching. However, several mismatches are ignored, i.e., mismatch in local visual representation and global sentence representation, and mismatch in visual space and corresponding label word space. In this paper, we propose crossmodal match for 3D

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Ujala Razaq, Muhammad Muneeb Ullah, and Muhammad Usman. "Local and Deep Features for Robust Visual Indoor Place Recognition." Open Journal of Science and Technology 3, no. 2 (2020): 140–47. http://dx.doi.org/10.31580/ojst.v3i2.1475.

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This study focuses on the area of visual indoor place recognition (e.g., in an office setting, automatically recognizing different places, such as offices, corridor, wash room, etc.). The potential applications include robot navigation, augmented reality, and image retrieval. However, the task is extremely demanding because of the variations in appearance in such dynamic setups (e.g., view-point, occlusion, illumination, scale, etc.). Recently, Convolutional Neural Network (CNN) has emerged as a powerful learning mechanism, able to learn deep higher-level features when provided with a comparat

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Forschack, Norman, Søren K. Andersen, and Matthias M. Müller. "Global Enhancement but Local Suppression in Feature-based Attention." Journal of Cognitive Neuroscience 29, no. 4 (2017): 619–27. http://dx.doi.org/10.1162/jocn_a_01075.

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A key property of feature-based attention is global facilitation of the attended feature throughout the visual field. Previously, we presented superimposed red and blue randomly moving dot kinematograms (RDKs) flickering at a different frequency each to elicit frequency-specific steady-state visual evoked potentials (SSVEPs) that allowed us to analyze neural dynamics in early visual cortex when participants shifted attention to one of the two colors. Results showed amplification of the attended and suppression of the unattended color as measured by SSVEP amplitudes. Here, we tested whether the

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Da, Zikai, Yu Gao, Zihan Xue, Jing Cao, and Peizhen Wang. "Local and Global Feature Aggregation-Aware Network for Salient Object Detection." Electronics 11, no. 2 (2022): 231. http://dx.doi.org/10.3390/electronics11020231.

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With the rise of deep learning technology, salient object detection algorithms based on convolutional neural networks (CNNs) are gradually replacing traditional methods. The majority of existing studies, however, focused on the integration of multi-scale features, thereby ignoring the characteristics of other significant features. To address this problem, we fully utilized the features to alleviate redundancy. In this paper, a novel CNN named local and global feature aggregation-aware network (LGFAN) has been proposed. It is a combination of the visual geometry group backbone for feature extra

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von der Heydt, Rüdiger, and Nan R. Zhang. "Figure and ground: how the visual cortex integrates local cues for global organization." Journal of Neurophysiology 120, no. 6 (2018): 3085–98. http://dx.doi.org/10.1152/jn.00125.2018.

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Inferring figure-ground organization in two-dimensional images may require different complementary strategies. For isolated objects, it has been shown that mechanisms in visual cortex exploit the overall distribution of contours, but in images of cluttered scenes where the grouping of contours is not obvious, that strategy would fail. However, natural scenes contain local features, specifically contour junctions, that may contribute to the definition of object regions. To study the role of local features in the assignment of border ownership, we recorded single-cell activity from visual cortex

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Zhou, Zhili, Meimin Wang, Yi Cao, and Yuecheng Su. "CNN Feature-Based Image Copy Detection with Contextual Hash Embedding." Mathematics 8, no. 7 (2020): 1172. http://dx.doi.org/10.3390/math8071172.

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As one of the important techniques for protecting the copyrights of digital images, content-based image copy detection has attracted a lot of attention in the past few decades. The traditional content-based copy detection methods usually extract local hand-crafted features and then quantize these features to visual words by the bag-of-visual-words (BOW) model to build an inverted index file for rapid image matching. Recently, deep learning features, such as the features derived from convolutional neural networks (CNN), have been proven to outperform the hand-crafted features in many applicatio

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Bilquees, Samina, Hassan Dawood, Hussain Dawood, Nadeem Majeed, Ali Javed, and Muhammad Tariq Mahmood. "Noise Resilient Local Gradient Orientation for Content-Based Image Retrieval." International Journal of Optics 2021 (July 14, 2021): 1–19. http://dx.doi.org/10.1155/2021/4151482.

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In a world of multimedia information, where users seek accurate results against search query and demand relevant multimedia content retrieval, developing an accurate content-based image retrieval (CBIR) system is difficult due to the presence of noise in the image. The performance of the CBIR system is impaired by this noise. To estimate the distance between the query and database images, CBIR systems use image feature representation. The noise or artifacts present within the visual data might confuse the CBIR when retrieving relevant results. Therefore, we propose Noise Resilient Local Gradie

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Lu, Feng, Shuting Dong, Lijun Zhang, et al. "Deep Homography Estimation for Visual Place Recognition." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 9 (2024): 10341–49. http://dx.doi.org/10.1609/aaai.v38i9.28901.

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Visual place recognition (VPR) is a fundamental task for many applications such as robot localization and augmented reality. Recently, the hierarchical VPR methods have received considerable attention due to the trade-off between accuracy and efficiency. They usually first use global features to retrieve the candidate images, then verify the spatial consistency of matched local features for re-ranking. However, the latter typically relies on the RANSAC algorithm for fitting homography, which is time-consuming and non-differentiable. This makes existing methods compromise to train the network o

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Mao, Keming, Renjie Tang, Xinqi Wang, Weiyi Zhang, and Haoxiang Wu. "Feature Representation Using Deep Autoencoder for Lung Nodule Image Classification." Complexity 2018 (2018): 1–11. http://dx.doi.org/10.1155/2018/3078374.

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This paper focuses on the problem of lung nodule image classification, which plays a key role in lung cancer early diagnosis. In this work, we propose a novel model for lung nodule image feature representation that incorporates both local and global characters. First, lung nodule images are divided into local patches with Superpixel. Then these patches are transformed into fixed-length local feature vectors using unsupervised deep autoencoder (DAE). The visual vocabulary is constructed based on the local features and bag of visual words (BOVW) is used to describe the global feature representat

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Deng, Ruizhe, Yang Zhao, and Yong Ding. "Hierarchical Feature Extraction Assisted with Visual Saliency for Image Quality Assessment." Journal of Engineering 2017 (2017): 1–11. http://dx.doi.org/10.1155/2017/4752378.

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Image quality assessment (IQA) is desired to evaluate the perceptual quality of an image in a manner consistent with subjective rating. Considering the characteristics of hierarchical visual cortex, a novel full reference IQA method is proposed in this paper. Quality-aware features that human visual system is sensitive to are extracted to describe image quality comprehensively. Concretely, log Gabor filters and local tetra patterns are employed to capture spatial frequency and local texture features, which are attractive to the primary and secondary visual cortex, respectively. Moreover, image

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Qu, Haicheng, Siqi Zhao, and Wanjun Liu. "Fine-Graine Visual Classification with Aggregated Object Localization and Salient Feature Suppression." Journal of Physics: Conference Series 2171, no. 1 (2022): 012036. http://dx.doi.org/10.1088/1742-6596/2171/1/012036.

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Abstract Fine-grained visual classification (FGVC) is desired to classify sub-classes of objects in the same super-class. For the FGVC tasks, it is necessary to find subtle yet discriminative information from local areas. However, traditional FGVC approaches tended to extract strong discriminative features, and overlook some subtle yet useful features. Besides, current methods ignore the influence of background noises on feature extraction. Therefore, aggregated object localization combined with salient feature suppression are proposed, which is a stacked network. First, the feature maps extra

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Yohanes, Banu Wirawan. "Images Similarity based on Bags of SIFT Descriptor and K-Means Clustering." Techné : Jurnal Ilmiah Elektroteknika 18, no. 02 (2019): 137–46. http://dx.doi.org/10.31358/techne.v18i02.217.

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The content based image retrieval is developed and receives many attention from computer vision, supported by the ubiquity of Internet and digital devices. Bag-of-words method from text-based image retrieval trains images’ local features to build visual vocabulary. These visual words are used to represent local features, then quantized before clustering into number of bags. Here, the scale invariant feature transform descriptor is used as local features of images that will be compared each other to find their similarity. It is robust to clutter and partial visibility compared to global feature

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Xu, Shuai Hua, Sheng Qi Guan, and Long Long Chen. "Steel Strip Defect Detection Based on Human Visual Attention Mechanism Model." Applied Mechanics and Materials 530-531 (February 2014): 456–62. http://dx.doi.org/10.4028/www.scientific.net/amm.530-531.456.

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According to the characteristics of steel strip, This paper propose the strip defect detection algorithm which is based on visual attention mechanism. First, extract the input image color, brightness and orientation characteristics and form simple feature map; secondly, prognosis on the features, get defective attention region by threshold segmentation to color characteristics of colored defect image. The wavelet decomposition to colorless defect image of brightness and direction features will form the multi-feature subgraph; then construct feature difference molecular graph through the featur

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Krishnan, Divya Lakshmi, Rajappa Muthaiah, Anand Madhukar Tapas, and Krithivasan Kannan. "Evaluation of Local Feature Detectors for the Comparison of Thermal and Visual Low Altitude Aerial Images." Defence Science Journal 68, no. 5 (2018): 473–79. http://dx.doi.org/10.14429/dsj.68.11233.

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Local features are key regions of an image suitable for applications such as image matching, and fusion. Detection of targets under varying atmospheric conditions, via aerial images is a typical defence application where multi spectral correlation is essential. Focuses on local features for the comparison of thermal and visual aerial images in this study. The state of the art differential and intensity comparison based features are evaluated over the dataset. An improved affine invariant feature is proposed with a new saliency measure. The performances of the existing and the proposed features

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Xiong, Jian, Xinzhong Zhu, Jie Yuan, Ran Shi, and Hao Gao. "Perceptual visual security assessment by fusing local and global feature similarity." Computers & Electrical Engineering 91 (May 2021): 107071. http://dx.doi.org/10.1016/j.compeleceng.2021.107071.

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Stach, Silke, Julie Benard, and Martin Giurfa. "Local-feature assembling in visual pattern recognition and generalization in honeybees." Nature 429, no. 6993 (2004): 758–61. http://dx.doi.org/10.1038/nature02594.

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Dragoi, Valentin, Jitendra Sharma, Earl K. Miller, and Mriganka Sur. "Dynamics of neuronal sensitivity in visual cortex and local feature discrimination." Nature Neuroscience 5, no. 9 (2002): 883–91. http://dx.doi.org/10.1038/nn900.

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Ren, Zhiquan, Yue Deng, and Qionghai Dai. "Local visual feature fusion via maximum margin multimodal deep neural network." Neurocomputing 175 (January 2016): 427–32. http://dx.doi.org/10.1016/j.neucom.2015.10.076.

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Zhang, Chunjie, Xian Xiao, Junbiao Pang, Chao Liang, Yifan Zhang, and Qingming Huang. "Beyond visual word ambiguity: Weighted local feature encoding with governing region." Journal of Visual Communication and Image Representation 25, no. 6 (2014): 1387–98. http://dx.doi.org/10.1016/j.jvcir.2014.05.010.

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Xiao, Feng, and Qiuxia Wu. "Visual saliency based global–local feature representation for skin cancer classification." IET Image Processing 14, no. 10 (2020): 2140–48. http://dx.doi.org/10.1049/iet-ipr.2019.1018.

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