Proceedings ArticleDOI
NMF-KNN: Image Annotation Using Weighted Multi-view Non-negative Matrix Factorization
Mahdi M. Kalayeh,Haroon Idrees,Mubarak Shah +2 more
- pp 184-191
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TLDR
The key idea is to learn query-specific generative model on the features of nearest-neighbors and tags using the proposed NMF-KNN approach which imposes consensus constraint on the coefficient matrices across different features to solve the problem of feature fusion.Abstract:
The real world image databases such as Flickr are characterized by continuous addition of new images. The recent approaches for image annotation, i.e. the problem of assigning tags to images, have two major drawbacks. First, either models are learned using the entire training data, or to handle the issue of dataset imbalance, tag-specific discriminative models are trained. Such models become obsolete and require relearning when new images and tags are added to database. Second, the task of feature-fusion is typically dealt using ad-hoc approaches. In this paper, we present a weighted extension of Multi-view Non-negative Matrix Factorization (NMF) to address the aforementioned drawbacks. The key idea is to learn query-specific generative model on the features of nearest-neighbors and tags using the proposed NMF-KNN approach which imposes consensus constraint on the coefficient matrices across different features. This results in coefficient vectors across features to be consistent and, thus, naturally solves the problem of feature fusion, while the weight matrices introduced in the proposed formulation alleviate the issue of dataset imbalance. Furthermore, our approach, being query-specific, is unaffected by addition of images and tags in a database. We tested our method on two datasets used for evaluation of image annotation and obtained competitive results.read more
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Journal ArticleDOI
Multi-omic and multi-view clustering algorithms: review and cancer benchmark
Nimrod Rappoport,Ron Shamir +1 more
TL;DR: This review covers methods developed specifically for omic data as well as generic multi-view methods developed in the machine learning community for joint clustering of multiple data types, providing the first systematic comparison of leading multi-omics and multi-View clustering algorithms.
Journal ArticleDOI
Robust Structured Nonnegative Matrix Factorization for Image Representation
Zechao Li,Jinhui Tang,Xiaofei He +2 more
TL;DR: This paper proposes a novel semisupervised NMF learning framework, called robust structured NMF, that learns a robust discriminative representation by leveraging the block-diagonal structure and the inline-formula-norm loss function, which addresses the problems of noise and outliers.
Journal ArticleDOI
Deploying Machine and Deep Learning Models for Efficient Data-Augmented Detection of COVID-19 Infections
Ahmed Sedik,Abdullah M. Iliyasu,Abdullah M. Iliyasu,Abdullah M. Iliyasu,Basma Abd El-Rahiem,Mohammed Elsayed Abdel Samea,Asmaa Abdel-Raheem,Mohamed Hammad,Jialiang Peng,Fathi E. Abd El-Samie,Ahmed A. Abd El-Latif,Ahmed A. Abd El-Latif +11 more
TL;DR: The proposed algorithm is effective in performing a rapid and consistent Corona virus diagnosis that is primarily aimed at assisting clinicians in making accurate identification of the virus.
Journal ArticleDOI
A Selective Review of Multi-Level Omics Data Integration Using Variable Selection.
TL;DR: This article focuses on reviewing existing multi-omics integration studies by paying special attention to variable selection methods, and reviews existing supervised, semi-supervised and unsupervised integrative analyses within parallel and hierarchical integration studies, respectively.
Journal ArticleDOI
Socializing the Semantic Gap: A Comparative Survey on Image Tag Assignment, Refinement, and Retrieval
TL;DR: In this paper, a comprehensive survey of content-based image retrieval focuses on what people tag about an image and how such information can be exploited to construct a tag relevance function. And a two-dimensional taxonomy is presented to structure the growing literature, understand the ingredients of the main works, clarify their connections and difference, and recognize their merits and limitations.
References
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Journal ArticleDOI
Learning the parts of objects by non-negative matrix factorization
TL;DR: An algorithm for non-negative matrix factorization is demonstrated that is able to learn parts of faces and semantic features of text and is in contrast to other methods that learn holistic, not parts-based, representations.
Learning parts of objects by non-negative matrix factorization
TL;DR: In this article, non-negative matrix factorization is used to learn parts of faces and semantic features of text, which is in contrast to principal components analysis and vector quantization that learn holistic, not parts-based, representations.
Proceedings Article
Algorithms for Non-negative Matrix Factorization
Daniel D. Lee,H. Sebastian Seung +1 more
TL;DR: Two different multiplicative algorithms for non-negative matrix factorization are analyzed and one algorithm can be shown to minimize the conventional least squares error while the other minimizes the generalized Kullback-Leibler divergence.
Journal ArticleDOI
Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope
Aude Oliva,Antonio Torralba +1 more
TL;DR: The performance of the spatial envelope model shows that specific information about object shape or identity is not a requirement for scene categorization and that modeling a holistic representation of the scene informs about its probable semantic category.
Journal ArticleDOI
Gradient Projection for Sparse Reconstruction: Application to Compressed Sensing and Other Inverse Problems
TL;DR: This paper proposes gradient projection algorithms for the bound-constrained quadratic programming (BCQP) formulation of these problems and test variants of this approach that select the line search parameters in different ways, including techniques based on the Barzilai-Borwein method.