Deep Features for training Support Vector Machine
- URL: http://arxiv.org/abs/2104.03488v1
- Date: Thu, 8 Apr 2021 03:13:09 GMT
- Title: Deep Features for training Support Vector Machine
- Authors: Loris Nanni, Stefano Ghidoni, Sheryl Brahnam
- Abstract summary: This paper develops a generic computer vision system based on features extracted from trained CNNs.
Multiple learned features are combined into a single structure to work on different image classification tasks.
- Score: 16.795405355504077
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Features play a crucial role in computer vision. Initially designed to detect
salient elements by means of handcrafted algorithms, features are now often
learned by different layers in Convolutional Neural Networks (CNNs). This paper
develops a generic computer vision system based on features extracted from
trained CNNs. Multiple learned features are combined into a single structure to
work on different image classification tasks. The proposed system was
experimentally derived by testing several approaches for extracting features
from the inner layers of CNNs and using them as inputs to SVMs that are then
combined by sum rule. Dimensionality reduction techniques are used to reduce
the high dimensionality of inner layers. The resulting vision system is shown
to significantly boost the performance of standard CNNs across a large and
diverse collection of image data sets. An ensemble of different topologies
using the same approach obtains state-of-the-art results on a virus data set.
Related papers
- Which Backbone to Use: A Resource-efficient Domain Specific Comparison for Computer Vision [4.600687314645625]
Architectural backbones pre-trained on large datasets like ImageNet are commonly employed as feature extractors.
Our study systematically evaluates multiple lightweight, pre-trained CNN backbones under consistent training settings.
Our findings provide actionable insights into the performance trade-offs and effectiveness of different backbones.
arXiv Detail & Related papers (2024-06-09T02:01:25Z) - Neural Clustering based Visual Representation Learning [61.72646814537163]
Clustering is one of the most classic approaches in machine learning and data analysis.
We propose feature extraction with clustering (FEC), which views feature extraction as a process of selecting representatives from data.
FEC alternates between grouping pixels into individual clusters to abstract representatives and updating the deep features of pixels with current representatives.
arXiv Detail & Related papers (2024-03-26T06:04:50Z) - Deep Image Clustering with Contrastive Learning and Multi-scale Graph
Convolutional Networks [58.868899595936476]
This paper presents a new deep clustering approach termed image clustering with contrastive learning and multi-scale graph convolutional networks (IcicleGCN)
Experiments on multiple image datasets demonstrate the superior clustering performance of IcicleGCN over the state-of-the-art.
arXiv Detail & Related papers (2022-07-14T19:16:56Z) - Two-Stream Graph Convolutional Network for Intra-oral Scanner Image
Segmentation [133.02190910009384]
We propose a two-stream graph convolutional network (i.e., TSGCN) to handle inter-view confusion between different raw attributes.
Our TSGCN significantly outperforms state-of-the-art methods in 3D tooth (surface) segmentation.
arXiv Detail & Related papers (2022-04-19T10:41:09Z) - Vision Transformer with Convolutions Architecture Search [72.70461709267497]
We propose an architecture search method-Vision Transformer with Convolutions Architecture Search (VTCAS)
The high-performance backbone network searched by VTCAS introduces the desirable features of convolutional neural networks into the Transformer architecture.
It enhances the robustness of the neural network for object recognition, especially in the low illumination indoor scene.
arXiv Detail & Related papers (2022-03-20T02:59:51Z) - Network Comparison Study of Deep Activation Feature Discriminability
with Novel Objects [0.5076419064097732]
State-of-the-art computer visions algorithms have incorporated Deep Neural Networks (DNN) in feature extracting roles, creating Deep Convolutional Activation Features (DeCAF)
This study analyzes the general discriminability of novel object visual appearances encoded into the DeCAF space of six of the leading visual recognition DNN architectures.
arXiv Detail & Related papers (2022-02-08T07:40:53Z) - Deep ensembles in bioimage segmentation [74.01883650587321]
In this work, we propose an ensemble of convolutional neural networks (CNNs)
In ensemble methods, many different models are trained and then used for classification, the ensemble aggregates the outputs of the single classifiers.
The proposed ensemble is implemented by combining different backbone networks using the DeepLabV3+ and HarDNet environment.
arXiv Detail & Related papers (2021-12-24T05:54:21Z) - The Mind's Eye: Visualizing Class-Agnostic Features of CNNs [92.39082696657874]
We propose an approach to visually interpret CNN features given a set of images by creating corresponding images that depict the most informative features of a specific layer.
Our method uses a dual-objective activation and distance loss, without requiring a generator network nor modifications to the original model.
arXiv Detail & Related papers (2021-01-29T07:46:39Z) - Combining pretrained CNN feature extractors to enhance clustering of
complex natural images [27.784346095205358]
This paper aims at providing insight on the use of pretrained CNN features for image clustering (IC)
To solve this issue, we propose to rephrase the IC problem as a multi-view clustering (MVC) problem.
We then propose a multi-input neural network architecture that is trained end-to-end to solve the MVC problem effectively.
arXiv Detail & Related papers (2021-01-07T21:23:04Z) - Convolutional Neural Networks for Multispectral Image Cloud Masking [7.812073412066698]
Convolutional neural networks (CNN) have proven to be state of the art methods for many image classification tasks.
We study the use of different CNN architectures for cloud masking of Proba-V multispectral images.
arXiv Detail & Related papers (2020-12-09T21:33:20Z)
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.