Journal ArticleDOI
Content based medical image retrieval using dictionary learning
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TLDR
An approach grouping similar images into clusters that are sparsely represented by the dictionaries and learning dictionaries simultaneously via K-SVD is proposed to group large medical databases to demonstrate the efficacy of the proposed method in the retrieval of medical images.About:
This article is published in Neurocomputing.The article was published on 2015-11-30. It has received 73 citations till now. The article focuses on the topics: K-SVD & Content-based image retrieval.read more
Citations
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Journal ArticleDOI
Content-Based Image Retrieval and Feature Extraction: A Comprehensive Review
Afshan Latif,Aqsa Rasheed,Umer Sajid,Jameel Ahmed,Nouman Ali,Naeem Iqbal Ratyal,Bushra Zafar,Bushra Zafar,Saadat Hanif Dar,Muhammad Sajid,Tehmina Khalil +10 more
TL;DR: A comprehensive review of the recent development in the area of CBIR and image representation is presented and the main aspects of various image retrieval and image representations models from low-level feature extraction to recent semantic deep-learning approaches are analyzed.
Journal ArticleDOI
Radiological images and machine learning: Trends, perspectives, and prospects.
Zhenwei Zhang,Ervin Sejdic +1 more
TL;DR: The fundamental concepts behind various machine learning techniques and their applications in several radiological imaging areas are covered, such as medical image segmentation, brain function studies and neurological disease diagnosis, as well as computer-aided systems, image registration, and content-based image retrieval systems.
Journal ArticleDOI
Content based image retrieval with sparse representations and local feature descriptors : A comparative study
Ceyhun Celik,Hasan Sakir Bilge +1 more
TL;DR: The most successful approach in the CBIR framework is to use LLC for Coil20 data set and FBSR for Corel1000 data set, and three methods recently proposed in literature (Online Dictionary Learning, Locality-constrained Linear Coding and Feature-based Sparse Representation) are tested and compared with the framework results.
Journal ArticleDOI
A survey on image data analysis through clustering techniques for real world applications
TL;DR: Significant feature extraction approaches and clustering methods applied on the image data from nine important applicative areas are reviewed and characteristics of images, suitable clustering approaches for each domain, challenges and future research directions for image clustering are discussed.
Proceedings ArticleDOI
A deep learning architecture for classifying medical images of anatomy object
Sameer Ahmad Khan,Suet-Peng Yong +1 more
TL;DR: Based on the experiments, it is shown that the proposed Convolutional Neural Network architecture outperforms the three milestone architectures in classifying medical images of anatomy object.
References
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Journal ArticleDOI
Textural Features for Image Classification
TL;DR: These results indicate that the easily computable textural features based on gray-tone spatial dependancies probably have a general applicability for a wide variety of image-classification applications.
Journal ArticleDOI
Matching pursuits with time-frequency dictionaries
Stéphane Mallat,Zhifeng Zhang +1 more
TL;DR: The authors introduce an algorithm, called matching pursuit, that decomposes any signal into a linear expansion of waveforms that are selected from a redundant dictionary of functions, chosen in order to best match the signal structures.
Journal ArticleDOI
$rm K$ -SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation
TL;DR: A novel algorithm for adapting dictionaries in order to achieve sparse signal representations, the K-SVD algorithm, an iterative method that alternates between sparse coding of the examples based on the current dictionary and a process of updating the dictionary atoms to better fit the data.
Journal ArticleDOI
Content-based image retrieval at the end of the early years
TL;DR: The working conditions of content-based retrieval: patterns of use, types of pictures, the role of semantics, and the sensory gap are discussed, as well as aspects of system engineering: databases, system architecture, and evaluation.
Book ChapterDOI
Improving the fisher kernel for large-scale image classification
TL;DR: In an evaluation involving hundreds of thousands of training images, it is shown that classifiers learned on Flickr groups perform surprisingly well and that they can complement classifier learned on more carefully annotated datasets.