Journal ArticleDOI
Discriminative features for image classification and retrieval
Shang Liu,Xiao Bai +1 more
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TLDR
A new method to improve the performance of current bag-of-word based image classification process by introducing a pairwise image matching scheme to select the discriminative features.About:
This article is published in Pattern Recognition Letters.The article was published on 2012-04-01. It has received 44 citations till now. The article focuses on the topics: Visual Word & Automatic image annotation.read more
Citations
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Journal ArticleDOI
Single image super resolution via neighbor reconstruction
TL;DR: A new regression-based SR algorithm that overcomes the limitations of A+ and benefits from an innovative and simple Neighbor Reconstruction Method (NRM), which reconstructs new patches which are closer to the anchor point in the manifold space.
Journal ArticleDOI
LUIFT: LUminance Invariant Feature Transform
TL;DR: Computer simulation results show that the proposed illumination-invariant method yields a superior feature detection and matching performance under illumination change, noise degradation, and slight geometric distortions comparing with that of the state-of-the-art descriptors.
Journal ArticleDOI
Discriminative sparse neighbor coding
TL;DR: This work establishes two modules to improve the discriminative ability of sparse representation, and incorporates the feature distribution into the objective function, spanning a class-specific low dimensional subspace for effective sparse coding.
Feature selection and classification approaches for biometric and biomedical applications
TL;DR: The focus of this thesis is to develop feature selection and classification models for biometric and biomedical problems that can be used in designing applications for predicting diseases and validate identity.
Proceedings Article
Object detection via foreground contour feature selection and part-based shape model
TL;DR: A novel approach for object detection via foreground feature selection and part-based shape model that automatically learns a shape model from cluttered training images without need to explicitly given bounding box on objects is proposed.
References
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Proceedings ArticleDOI
ImageNet: A large-scale hierarchical image database
TL;DR: A new database called “ImageNet” is introduced, a large-scale ontology of images built upon the backbone of the WordNet structure, much larger in scale and diversity and much more accurate than the current image datasets.
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.
Distinctive Image Features from Scale-Invariant Keypoints
TL;DR: The Scale-Invariant Feature Transform (or SIFT) algorithm is a highly robust method to extract and consequently match distinctive invariant features from images that can then be used to reliably match objects in diering images.
Journal ArticleDOI
Speeded-Up Robust Features (SURF)
TL;DR: A novel scale- and rotation-invariant detector and descriptor, coined SURF (Speeded-Up Robust Features), which approximates or even outperforms previously proposed schemes with respect to repeatability, distinctiveness, and robustness, yet can be computed and compared much faster.