Image categorization: Graph edit distance+edge direction histogram
Pattern Recognition
KPCA for semantic object extraction in images
Pattern Recognition
State-of-the-art on spatio-temporal information-based video retrieval
Pattern Recognition
A genetic programming framework for content-based image retrieval
Pattern Recognition
Stochastic modeling western paintings for effective classification
Pattern Recognition
A new feature selection method for Gaussian mixture clustering
Pattern Recognition
Texture image retrieval based on non-tensor product wavelet filter banks
Signal Processing
Techniques for efficient and effective transformed image identification
Journal of Visual Communication and Image Representation
Fast Haar transform based feature extraction for face representation and recognition
IEEE Transactions on Information Forensics and Security
Iterative subspace analysis based on feature line distance
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Proceedings of the 2010 Symposium on Eye-Tracking Research & Applications
An integrated aurora image retrieval system: AuroraEye
Journal of Visual Communication and Image Representation
Intelligent Social Media Indexing and Sharing Using an Adaptive Indexing Search Engine
ACM Transactions on Intelligent Systems and Technology (TIST)
Spatial feature interdependence matrix (SFIM): a robust descriptor for face recognition
PSIVT'11 Proceedings of the 5th Pacific Rim conference on Advances in Image and Video Technology - Volume Part I
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With many potential industrial applications, content-based image retrieval (CBIR) has recently gained more attention for image management and web searching. As an important tool to capture users' preferences and thus to improve the performance of CBIR systems, a variety of relevance feedback (RF) schemes have been developed in recent years. One key issue in RF is: which features (or feature dimensions) can benefit this human-computer iteration procedure? In this paper, we make theoretical and practical comparisons between principal and complement components of image features in CBIR RF. Most of the previous RF approaches treat the positive and negative feedbacks equivalently although this assumption is not appropriate since the two groups of training feedbacks have very different properties. That is, all positive feedbacks share a homogeneous concept while negative feedbacks do not. We explore solutions to this important problem by proposing an orthogonal complement component analysis. Experimental results are reported on a real-world image collection to demonstrate that the proposed complement components method consistently outperforms the conventional principal components method in both linear and kernel spaces when users want to retrieve images with a homogeneous concept.