Efficient and effective querying by image content
Journal of Intelligent Information Systems - Special issue: advances in visual information management systems
Video parsing, retrieval and browsing: an integrated and content-based solution
Proceedings of the third ACM international conference on Multimedia
Machine Learning
VisualSEEk: a fully automated content-based image query system
MULTIMEDIA '96 Proceedings of the fourth ACM international conference on Multimedia
Unsupervised Segmentation of Color-Texture Regions in Images and Video
IEEE Transactions on Pattern Analysis and Machine Intelligence
SIMPLIcity: Semantics-Sensitive Integrated Matching for Picture LIbraries
IEEE Transactions on Pattern Analysis and Machine Intelligence
On Image Classification: City vs. Landscape
CBAIVL '98 Proceedings of the IEEE Workshop on Content - Based Access of Image and Video Libraries
NeTra: a toolbox for navigating large image databases
ICIP '97 Proceedings of the 1997 International Conference on Image Processing (ICIP '97) 3-Volume Set-Volume 1 - Volume 1
Supporting Content-based Queries over Images in MARS
ICMCS '97 Proceedings of the 1997 International Conference on Multimedia Computing and Systems
Image classification using hybrid neural networks
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Principal Component Analysis of Multispectral Images Using Neural Network
AICCSA '01 Proceedings of the ACS/IEEE International Conference on Computer Systems and Applications
Automatic Linguistic Indexing of Pictures by a Statistical Modeling Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Content-based image classification using a neural network
Pattern Recognition Letters
A tutorial on support vector regression
Statistics and Computing
Region-Based Image Retrieval with High-Level Semantic Color Names
MMM '05 Proceedings of the 11th International Multimedia Modelling Conference
Classification of acoustic events using SVM-based clustering schemes
Pattern Recognition
RBF-based neurodynamic nearest neighbor classification in real pattern space
Pattern Recognition
Expert Systems with Applications: An International Journal
Neurocomputing
AdaBoost with SVM-based component classifiers
Engineering Applications of Artificial Intelligence
Robust and efficient multiclass SVM models for phrase pattern recognition
Pattern Recognition
Combining intra-image and inter-class semantics for consumer image retrieval
Pattern Recognition
Image classification for content-based indexing
IEEE Transactions on Image Processing
CLUE: cluster-based retrieval of images by unsupervised learning
IEEE Transactions on Image Processing
Principal components null space analysis for image and video classification
IEEE Transactions on Image Processing
Image interpolation for progressive transmission by using radial basis function networks
IEEE Transactions on Neural Networks
Support vector machines for histogram-based image classification
IEEE Transactions on Neural Networks
Modeling, classifying and annotating weakly annotated images using Bayesian network
Journal of Visual Communication and Image Representation
Expert Systems with Applications: An International Journal
Maximum power point tracking (MPPT) system of small wind power generator using RBFNN approach
Expert Systems with Applications: An International Journal
Sparse multikernel support vector regression machines trained by active learning
Expert Systems with Applications: An International Journal
Recurrent sparse support vector regression machines trained by active learning in the time-domain
Expert Systems with Applications: An International Journal
An annotation rule extraction algorithm for image retrieval
Pattern Recognition Letters
Hi-index | 12.06 |
Digital cameras and thus digital images are now ubiquitous. How to efficiently manage a large amount of images has become important. The semantic analysis of images is an important issue in multimedia processing. Region-based image retrieval systems attempt to reduce the gap between high-level semantics and low-level features by representing images at the object level. Recently, the support vector machine (SVM) has been proposed to solve the classification problem. It can generate a hyperplane to separate two sets of features and provides good generalization performance. In this paper, we propose a novel method which integrates principal component analysis (PCA) and SVM neural networks for analyzing the semantic content of natural images, in which principal component analysis (PCA) is applied to reduce the dimension of features. Experimental results show that the proposed method is capable of analyzing the components of photographs into semantic categories with high accuracy, resulting in photographic analysis that is similar to human perception. The performance of the proposed method is better than that of the traditional radial basis function (RBF) neural network.