Local Features Applied to Dermoscopy Images: Bag-of-Features versus Sparse Coding

2017 ◽  
pp. 528-536 ◽  
Author(s):  
Catarina Barata ◽  
Mário A. T. Figueiredo ◽  
M. Emre Celebi ◽  
Jorge S. Marques
Keyword(s):  
Sparse Coding ◽  
Local Features ◽  
Bag Of Features

Supervised learning of bag-of-features shape descriptors using sparse coding

2014 ◽  
Vol 33 (5) ◽  
pp. 127-136 ◽  
Author(s):  
Roee Litman ◽  
Alex Bronstein ◽  
Michael Bronstein ◽  
Umberto Castellani
Keyword(s):  
Supervised Learning ◽  
Sparse Coding ◽  
Shape Descriptors ◽  
Bag Of Features

scholarly journals Appending Global to Local features for Skin Lesion Classification on Dermoscpic Images

2021 ◽  
Author(s):  
Dr. Ahlam Fadhil Mahmood ◽  
◽  
Hamed Abdulaziz Mahmood ◽  
Keyword(s):  
Skin Cancer ◽  
Skin Lesion ◽  
Classification Accuracy ◽  
Local Features ◽  
Fuzzy Rules ◽  
The Novel ◽  
Bag Of Features ◽  
Global Form ◽  
Melanoma Diagnosis ◽  
Novel Approach

Skin cancer is the deadliest diseases compared with all other kinds of cancer. In this paper various pre- and post-treatments are proposed for improving automated melanoma diagnosis of dermoscopy images. At first pre-processing have done to exclude unwanted parts, a new triple-A segmentation proposes to extract lesion according to their histogram patterns. Lastly, suggest appending process with testing many factors for superior detection decision. This paper offers a novel approach with testing different detection rules: first system used fuzzy rules based on a different features, a second test has been done by modeled local colours with bag-of-features classifier. Then proposed adding lesion shape on two previous systems as their global form in the first one, while distributing it and appending with local colour patches in the second system. For each case, different features; various colour models, and many other parameters are examined to decide which settings are more discriminating. Evaluates performance of each method has carried out on (ISIC2019 Challenge) dermoscopic database. The novel processes with their a specific parameters are rising the classification accuracy to 98.26%.

Ear recognition via sparse coding of local features

2018 ◽  
Vol 27 (01) ◽  
pp. 1 ◽  
Author(s):  
Mohamad Mahmoud Al Rahhal ◽  
Mohamed Lamine Mekhalfi ◽  
Taghreed Abdullah Mohammed Ali
Keyword(s):  
Sparse Coding ◽  
Local Features ◽  
Ear Recognition

Local Spatiotemporal Coding and Sparse Representation Based Human Action Recognition

2013 ◽  
Vol 401-403 ◽  
pp. 1555-1560
Author(s):  
Bin Wang ◽  
Yu Liu ◽  
Wei Wang ◽  
Wei Xu ◽  
Mao Jun Zhang
Keyword(s):  
Sparse Representation ◽  
Action Recognition ◽  
Human Action Recognition ◽  
Human Action ◽  
Local Features ◽  
Space Time ◽  
Video Classification ◽  
Bag Of Features ◽  
Temporal Relationships ◽  
Multi Level

To handle with the limitation of bag-of-features (BoF) model which ignores spatial and temporal relationships of local features in human action recognition in video, a Local Spatiotemporal Coding (LSC) is proposed. Rather than the exiting methods only uses the feature appearance information for coding, LSC encodes feature appearance and spatiotemporal positions information simultaneously with vector quantization (VQ). It can directly models the spatiotemporal relationships of local features in space time volume (STV). In implement, the local features are projected into sub-space-time-volume (sub-STV), and encoded with LSC. In addition a multi-level LSC is also provided. Then a group of sub-STV descriptors obtained from videos with multi-level LSC and Avg-pooling are used for action video classification. A sparse representation based classification method is adopted to classify action videos upon these sub-STV descriptors. The experimental results on KTH, Weizmann, and UCF sports datasets show that our method achieves better performance than the previous local spatiotemporal features based human action recognition methods.

scholarly journals Image restoration based on sparse representation using feature classification learning

2020 ◽  
Vol 2020 (1) ◽  
Author(s):  
Minhui Chang ◽  
Lei Zhang
Keyword(s):  
Singular Value Decomposition ◽  
Image Restoration ◽  
Sparse Representation ◽  
Sparse Coding ◽  
Local Features ◽  
Singular Value ◽  
Superior Performance ◽  
Classification Learning ◽  
Feature Classification ◽  
Value Decomposition

Abstract In the image inpainting method based on sparse representation, the adaptability of over-complete dictionary has a great influence on the result of image restoration. If the over-complete dictionary cannot effectively reflect the differences between different local features, it may result in the loss of texture details, resulting in blurred or over-smooth phenomenon in restored images. In view of these problems, we propose an image restoration method based on sparse representation using feature classification learning. Firstly, we perform singular value decomposition on the local gradient vector. According to the relationship between the main orientation and the secondary orientation, we classify all the local patches into three categories: smooth patch, edge patch and texture patch. Secondly, we use K-Singular Value Decomposition method to learn over-complete dictionaries that adapt to different features. Finally, we use Orthogonal Matching Pursuit method to calculate the sparse coding of target patches with different local features on their corresponding over-complete dictionaries, and use the over-complete dictionary and corresponding sparse coding to restore the damaged pixels. A series of experiments on various restoration tasks show the superior performance of the proposed method.

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