Enhancing Person Re-Identification through Tensor Feature Fusion

• URL: http://arxiv.org/abs/2312.10470v1
• Date: Sat, 16 Dec 2023 15:04:07 GMT
• Title: Enhancing Person Re-Identification through Tensor Feature Fusion
• Authors: Akram Abderraouf Gharbi, Ammar Chouchane, Mohcene Bessaoudi, Abdelmalik Ouamane, El ouanas Belabbaci
• Abstract summary: We present a novel person reidentification (PRe-ID) system that based on tensor feature representation and multilinear subspace learning. Our approach utilizes pretrained CNNs for high-level feature extraction. Cross-View Quadratic Discriminant Analysis (TXQDA) algorithm is used for multilinear subspace learning.
• Score: 0.5562294018150907
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we present a novel person reidentification (PRe-ID) system that based on tensor feature representation and multilinear subspace learning. Our approach utilizes pretrained CNNs for high-level feature extraction, along with Local Maximal Occurrence (LOMO) and Gaussian Of Gaussian (GOG ) descriptors. Additionally, Cross-View Quadratic Discriminant Analysis (TXQDA) algorithm is used for multilinear subspace learning, which models the data in a tensor framework to enhance discriminative capabilities. Similarity measure based on Mahalanobis distance is used for matching between training and test pedestrian images. Experimental evaluations on VIPeR and PRID450s datasets demonstrate the effectiveness of our method.

Related papers

• Hierarchical Visual Categories Modeling: A Joint Representation Learning and Density Estimation Framework for Out-of-Distribution Detection [28.442470704073767]
  This paper proposes a novel hierarchical visual category modeling scheme to separate out-of-distribution data from in-distribution data. We conduct experiments on seven popular benchmarks, including CIFAR, iNaturalist, SUN, Places, Textures, ImageNet-O, and OpenImage-O. Our visual representation has a competitive performance when compared with features learned by classical methods.
  arXiv  Detail & Related papers  (2024-08-28T07:05:46Z)
• Exploring Beyond Logits: Hierarchical Dynamic Labeling Based on Embeddings for Semi-Supervised Classification [49.09505771145326]
  We propose a Hierarchical Dynamic Labeling (HDL) algorithm that does not depend on model predictions and utilizes image embeddings to generate sample labels. Our approach has the potential to change the paradigm of pseudo-label generation in semi-supervised learning.
  arXiv  Detail & Related papers  (2024-04-26T06:00:27Z)
• Advancing Person Re-Identification: Tensor-based Feature Fusion and Multilinear Subspace Learning [0.6829272097221595]
  We propose a novel PRe-ID system that combines feature representation and multilinear subspace learning. Our method exploits the power of pre-trained Conrimial Neural Networks (CNNs) as a strong deep feature extractor. We evaluate our approach by conducting experiments on three datasets VIPeR, GRID, and PRID450s.
  arXiv  Detail & Related papers  (2023-12-24T16:19:22Z)
• Enhancing Cross-Dataset Performance of Distracted Driving Detection With Score-Softmax Classifier [7.302402275736439]
  Deep neural networks enable real-time monitoring of in-vehicle driver, facilitating the timely prediction of distractions, fatigue, and potential hazards. Recent research has exposed unreliable cross-dataset end-to-end driver behavior recognition due to overfitting. We introduce the Score-Softmax classifier, which addresses this issue by enhancing inter-class independence and Intra-class uncertainty.
  arXiv  Detail & Related papers  (2023-10-08T15:28:01Z)
• Convolutional autoencoder-based multimodal one-class classification [80.52334952912808]
  One-class classification refers to approaches of learning using data from a single class only. We propose a deep learning one-class classification method suitable for multimodal data.
  arXiv  Detail & Related papers  (2023-09-25T12:31:18Z)
• Learning disentangled representations for explainable chest X-ray classification using Dirichlet VAEs [68.73427163074015]
  This study explores the use of the Dirichlet Variational Autoencoder (DirVAE) for learning disentangled latent representations of chest X-ray (CXR) images. The predictive capacity of multi-modal latent representations learned by DirVAE models is investigated through implementation of an auxiliary multi-label classification task.
  arXiv  Detail & Related papers  (2023-02-06T18:10:08Z)
• Deep Neural Network Classifier for Multi-dimensional Functional Data [4.340040784481499]
  We propose a new approach, called as functional deep neural network (FDNN), for classifying multi-dimensional functional data. Specifically, a deep neural network is trained based on the principle components of the training data which shall be used to predict the class label of a future data function.
  arXiv  Detail & Related papers  (2022-05-17T19:22:48Z)
• Clustering augmented Self-Supervised Learning: Anapplication to Land Cover Mapping [10.720852987343896]
  We introduce a new method for land cover mapping by using a clustering based pretext task for self-supervised learning. We demonstrate the effectiveness of the method on two societally relevant applications.
  arXiv  Detail & Related papers  (2021-08-16T19:35:43Z)
• No Fear of Heterogeneity: Classifier Calibration for Federated Learning with Non-IID Data [78.69828864672978]
  A central challenge in training classification models in the real-world federated system is learning with non-IID data. We propose a novel and simple algorithm called Virtual Representations (CCVR), which adjusts the classifier using virtual representations sampled from an approximated ssian mixture model. Experimental results demonstrate that CCVR state-of-the-art performance on popular federated learning benchmarks including CIFAR-10, CIFAR-100, and CINIC-10.
  arXiv  Detail & Related papers  (2021-06-09T12:02:29Z)
• Spatial-spectral Hyperspectral Image Classification via Multiple Random Anchor Graphs Ensemble Learning [88.60285937702304]
  This paper proposes a novel spatial-spectral HSI classification method via multiple random anchor graphs ensemble learning (RAGE) Firstly, the local binary pattern is adopted to extract the more descriptive features on each selected band, which preserves local structures and subtle changes of a region. Secondly, the adaptive neighbors assignment is introduced in the construction of anchor graph, to reduce the computational complexity.
  arXiv  Detail & Related papers  (2021-03-25T09:31:41Z)
• Learned Factor Graphs for Inference from Stationary Time Sequences [107.63351413549992]
  We propose a framework that combines model-based algorithms and data-driven ML tools for stationary time sequences. neural networks are developed to separately learn specific components of a factor graph describing the distribution of the time sequence. We present an inference algorithm based on learned stationary factor graphs, which learns to implement the sum-product scheme from labeled data.
  arXiv  Detail & Related papers  (2020-06-05T07:06:19Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.