Feature Selection for Fast Image Classification with Support Vector Machines

Fan, Zenghua; Wang, Kai‐An; Lu, Bao-Liang

doi:10.1007/978-3-540-30499-9_159

Cited by 11 publications

(11 citation statements)

References 6 publications

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“…The current image processing framework generates high-dimensional vectors to describe the spatial configuration of features extracted on single images. In the future, we plan to investigate to apply feature selection techniques [12,44] to reduce the size of extracted descriptors to achieve higher efficiency. Also, we only develop a simple two-class (close vs. far) image classification algorithm, therefore photos taken at intermediate distances sometimes cannot be correctly classified.…”

Section: Discussionmentioning

confidence: 99%

Learning human photo shooting patterns from large-scale community photo collections

Cao

O’Halloran

2014

Multimed Tools Appl

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Social photo sharing platforms on the Internet (e.g. Flickr) host billions of publicly accessible photos captured by millions of individual users from all over the world. These user-contributed and geo-tagged photo collections provide insights into human sociocultural life and provide important clues for understanding people's engagement and reaction to places and events around the world today. In this paper, we analyze over 2 million geo-tagged images uploaded by 12,000 individual Flickr users to investigate the photograph shooting patterns of different user groups; that is, tourist and local, Asian and European, and male and female users. Specifically, we make use of visual features extracted on single monocular images and their spatial configurations to infer 3D depth information of the photographs to establish the preferred shooting scale (close-up or far-distant) of the user groups. The results reveal which objects and scenes interest different groups of people and how these preferences change over space and time. As such, the research offers a new approach to the human sciences which study the individual, groups and society.

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Section: Discussionmentioning

confidence: 99%

Learning human photo shooting patterns from large-scale community photo collections

Cao

O’Halloran

2014

Multimed Tools Appl

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“…According to the feature ranking criterion, we can select the most discriminative features for the binary classification task. Furthermore, support vectors can be used as evidence for feature ranking [6] [3]. Assume the distance between the optimal hyperplane and the support vectors is ∆, the optimal hyperplane can be viewed as a kind of ∆-margin separating hyperplane which is located in the center of margin (−∆, ∆).…”

Section: Feature Selection In Binary Classificationmentioning

confidence: 99%

Fast recognition of multi-view faces with feature selection

Fan

2005

Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1

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“…As an effective classifier for identification, the support vector machine (SVM) classifier is well suited for signature modeling ( 10 ). Guyon et al ( 11 ) applied the SVM classifier to select feature genes from DNA microarrays, and the selected genes were proved to exhibit a greater classification performance.…”

Section: Introductionmentioning

confidence: 99%

“…Guyon et al ( 11 ) applied the SVM classifier to select feature genes from DNA microarrays, and the selected genes were proved to exhibit a greater classification performance. Fan et al ( 10 ) demonstrated that the SVM classifier for feature gene selection was able to speed up the classification process and the generalization performance.…”

Section: Introductionmentioning

confidence: 99%

Feature genes in metastatic breast cancer identified by MetaDE and SVM classifier methods

Tuo

Zhang

2018

Mol Med Report

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The aim of the present study was to investigate the feature genes in metastatic breast cancer samples. A total of 5 expression profiles of metastatic breast cancer samples were downloaded from the Gene Expression Omnibus database, which were then analyzed using the MetaQC and MetaDE packages in R language. The feature genes between metastasis and non-metastasis samples were screened under the threshold of P<0.05. Based on the protein-protein interactions (PPIs) in the Biological General Repository for Interaction Datasets, Human Protein Reference Database and Biomolecular Interaction Network Database, the PPI network of the feature genes was constructed. The feature genes identified by topological characteristics were then used for support vector machine (SVM) classifier training and verification. The accuracy of the SVM classifier was then evaluated using another independent dataset from The Cancer Genome Atlas database. Finally, function and pathway enrichment analyses for genes in the SVM classifier were performed. A total of 541 feature genes were identified between metastatic and non-metastatic samples. The top 10 genes with the highest betweenness centrality values in the PPI network of feature genes were Nuclear RNA Export Factor 1, cyclin-dependent kinase 2 (CDK2), myelocytomatosis proto-oncogene protein (MYC), Cullin 5, SHC Adaptor Protein 1, Clathrin heavy chain, Nucleolin, WD repeat domain 1, proteasome 26S subunit non-ATPase 2 and telomeric repeat binding factor 2. The cyclin-dependent kinase inhibitor 1A (CDKN1A), E2F transcription factor 1 (E2F1), and MYC interacted with CDK2. The SVM classifier constructed by the top 30 feature genes was able to distinguish metastatic samples from non-metastatic samples [correct rate, specificity, positive predictive value and negative predictive value >0.89; sensitivity >0.84; area under the receiver operating characteristic curve (AUROC) >0.96]. The verification of the SVM classifier in an independent dataset (35 metastatic samples and 143 non-metastatic samples) revealed an accuracy of 94.38% and AUROC of 0.958. Cell cycle associated functions and pathways were the most significant terms of the 30 feature genes. A SVM classifier was constructed to assess the possibility of breast cancer metastasis, which presented high accuracy in several independent datasets. CDK2, CDKN1A, E2F1 and MYC were indicated as the potential feature genes in metastatic breast cancer.

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Feature Selection for Fast Image Classification with Support Vector Machines

Cited by 11 publications

References 6 publications

Learning human photo shooting patterns from large-scale community photo collections

Learning human photo shooting patterns from large-scale community photo collections

Fast recognition of multi-view faces with feature selection

Feature genes in metastatic breast cancer identified by MetaDE and SVM classifier methods

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