A principal component analysis-based feature dimensionality reduction scheme for content-based image retrieval system

Abstract

In content-based image retrieval (CBIR) system, one approach of image representation is to employ combination of low-level visual features cascaded together into a flat vector. While this presents more descriptive information, it however poses serious challenges in terms of high dimensionality and high computational cost of feature extraction algorithms to deployment of CBIR on platforms (devices) with limited computational and storage resources. Hence, in this work a feature dimensionality reduction technique based on principal component analysis (PCA) is implemented. Each image in a database is indexed using 174-dimensional feature vector comprising of 54-dimensional colour moments (CM54), 32-bin HSV-histogram (HIST32), 48-dimensional gabor wavelet (GW48) and 40-dimensional wavelet moments (MW40). The PCA scheme was incorporated into a CBIR system that utilized the entire feature vector space. The k-largest eigenvalues that yielded a not more than 5% degradation in mean precision were retained for dimensionality reduction. Three image databases (DB10, DB20 and DB100) were used for testing. The result obtained showed that with 80% reduction in feature dimensions, tolerable loss of 3.45, 4.39 and 7.40% in mean precision value were achieved on DB10, DB20 and DB100.

Freshness assessment of tilapia fish in traditional market based on an electronic nose

“…PCA is an effective unsupervised multivariate analysis that can be applied to transform data with high-dimension (many variables) into lowdimensional spaces without losing data characteristics. PCA commonly be used in various analyses, such as for data analysis on an e-nose device [29] and also be used in the same research on image processing [30]. An illustration of the PCA analysis when applied on an e-nose is presented in [20].…”

Clustering using kernel entropy principal component analysis and variable kernel estimator

“…As a solution, we consider the reduction of the dimensionality as an efficient preprocessing. Due to this effect, many researches have been done to get rid of this complication [18][19][20][21]. Thus, we establish a nonlinear method named KEPCA, which improves the existed linear EPCA method [13].…”

“…Due to the fact of analyzing data in term of feature extraction and dimensionality reduction, PCA is adopted by almost all disciplines. The ultimate objective of PCA is to select variables from input data table which have higher statistical information then squeeze out this information as a set of new orthogonal variables called principal component based on mathematic notions: eigenvalues, eigenvectors, mean and standard deviation [14,20].…”

Hand-Gesture Detection Using Principal Component Analysis (PCA) and Adaptive Neuro-Fuzzy Inference System (ANFIS)

“…Principal component analysis is a technique of reducing multivariant data (a lot of data) to convert an initial or original data matrix into a set of linear combinations that are fewer but absorb most of the variance from the initial data [7].…”