Journal ArticleDOI
PCA as Dimensionality Reduction for Large-Scale Image Retrieval Systems
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TLDR
Comparing multiple sets of data sets comparing dimensionality Reduction, Large-Scale Image Retrieval, Principal Component Analysis, Scale Invariant Feature Transform, Speeded Up Robust Features, and concluding that the PCA can beeffectively reduced.Abstract:
Dimensionalityreductioninlarge-scaleimageresearchplaysanimportantrolefortheirperformance in different applications. In this paper, we explore Principal Component Analysis (PCA) as a dimensionality reduction method. For this purpose, first, the Scale Invariant Feature Transform (SIFT)featuresandSpeededUpRobustFeatures(SURF)areextractedasimagefeatures.Second, thePCAisappliedtoreducethedimensionsofSIFTandSURFfeaturedescriptors.Bycomparing multiplesetsofexperimentaldatawithdifferentimagedatabases,wehaveconcludedthatPCAwitha reductionintherange,caneffectivelyreducethecomputationalcostofimagefeatures,andmaintain thehighretrievalperformanceaswell KeywoRDS Dimensionality Reduction, Large-Scale Image Retrieval, Principal Component Analysis, Scale Invariant Feature Transform, Speeded Up Robust Featuresread more
Citations
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Journal ArticleDOI
PreDTIs: prediction of drug-target interactions based on multiple feature information using gradient boosting framework with data balancing and feature selection techniques.
S. M. Hasan Mahmud,Wenyu Chen,Yongsheng Liu,Abdul Awal,Kawsar Ahmed,Habibur Rahman,Mohammad Ali Moni +6 more
TL;DR: Zhang et al. as discussed by the authors proposed a novel drug-target interaction prediction method called PreDTIs, where the feature vectors of the protein sequence are extracted by the pseudo-position-specific scoring matrix (PsePSSM), dipeptide composition (DC) and pseudo amino acid composition(PseAAC), and the drug is encoded with MACCS substructure fingerings.
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Improving Time Complexity and Accuracy of the Machine Learning Algorithms Through Selection of Highly Weighted Top k Features from Complex Datasets
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References
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Journal ArticleDOI
Distinctive Image Features from Scale-Invariant Keypoints
TL;DR: This paper presents a method for extracting distinctive invariant features from images that can be used to perform reliable matching between different views of an object or scene and can robustly identify objects among clutter and occlusion while achieving near real-time performance.
Journal ArticleDOI
Eigenfaces vs. Fisherfaces: recognition using class specific linear projection
TL;DR: A face recognition algorithm which is insensitive to large variation in lighting direction and facial expression is developed, based on Fisher's linear discriminant and produces well separated classes in a low-dimensional subspace, even under severe variations in lighting and facial expressions.
Journal ArticleDOI
Nonlinear component analysis as a kernel eigenvalue problem
TL;DR: A new method for performing a nonlinear form of principal component analysis by the use of integral operator kernel functions is proposed and experimental results on polynomial feature extraction for pattern recognition are presented.
Proceedings ArticleDOI
PCA-SIFT: a more distinctive representation for local image descriptors
Yan Ke,Rahul Sukthankar +1 more
TL;DR: This paper examines (and improves upon) the local image descriptor used by SIFT, and demonstrates that the PCA-based local descriptors are more distinctive, more robust to image deformations, and more compact than the standard SIFT representation.
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