Proceedings ArticleDOI
Duplication Detection for Image Sharing Systems
Reads0
Chats0
TLDR
A novel technique to detect duplication of images by matching the Speeded Up Robust Features of a query image to the feature set of images in the database, which are pre-computed, dimensionality reduced, and indexed.Abstract:
Duplication of images is a common occurrence in community based data sharing systems. An image of the same scene, residing as multiple copies in the system, introduces redundancy. This paper describes a novel technique to detect such submissions by matching the Speeded Up Robust Features (SURF) of a query image to the feature set of images in the database, which are pre-computed, dimensionality reduced, and indexed. First, a set of similar images is obtained with their feature key-point correspondences by computing homography. An occurrence of duplication is verified by statistical hypothesis testing, which considers the distribution obtained by inter-key-point Euclidean distance ratios between the corresponding key-points among the query and candidate images.read more
Citations
More filters
Book ChapterDOI
Ontology-Driven Content-Based Retrieval of Heritage Images
TL;DR: An ontology-driven content-based image retrieval system that follows bag of visual words model to recollect near-similar images from the database and the inclusion of ontology to prune the search space of CBIR system is observed to provide a considerable improvement in the performance.
Proceedings ArticleDOI
A Compact Mobile Image Quality Assessment Using A Simple Frequency Signature
TL;DR: The effectiveness of the proposed MIQA signature is experimentally validated by various experiments on several public datasets with subjective image quality evaluation and the proposed representation is optimal for all three IQA.
References
More filters
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
Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography
TL;DR: New results are derived on the minimum number of landmarks needed to obtain a solution, and algorithms are presented for computing these minimum-landmark solutions in closed form that provide the basis for an automatic system that can solve the Location Determination Problem under difficult viewing.
Proceedings ArticleDOI
Object recognition from local scale-invariant features
TL;DR: Experimental results show that robust object recognition can be achieved in cluttered partially occluded images with a computation time of under 2 seconds.
Book
Multiple view geometry in computer vision
Richard Hartley,Andrew Zisserman +1 more
TL;DR: In this article, the authors provide comprehensive background material and explain how to apply the methods and implement the algorithms directly in a unified framework, including geometric principles and how to represent objects algebraically so they can be computed and applied.
Reference EntryDOI
Principal Component Analysis
TL;DR: Principal component analysis (PCA) as discussed by the authors replaces the p original variables by a smaller number, q, of derived variables, the principal components, which are linear combinations of the original variables.