A Hybrid GA-LDA Scheme for Feature Selection in Content-Based Image Retrieval

2018 ◽  
Vol 9 (2) ◽  
pp. 48-71 ◽  
Author(s):  
Khadidja Belattar ◽  
Sihem Mostefai ◽  
Amer Draa
Keyword(s):  
Feature Selection ◽  
Image Retrieval ◽  
Nearest Neighbor ◽  
Skin Lesions ◽  
Processing Technique ◽  
Input Image ◽  
Content Based Image Retrieval ◽  
K Nearest Neighbor ◽  
Feature Selection Problem ◽  
Linear Discriminant

Feature selection is an important pre-processing technique in the pattern recognition domain. This article proposes a hybridization between Genetic Algorithm (GA) and the Linear Discriminant Analysis (LDA) for solving the feature selection problem in Content-Based Image Retrieval (CBIR) applied to dermatological images. In the first step, we preprocess and segment the input image, then we derive color and texture features characterizing healthy skin and the segmented skin lesion. At this stage, a binary GA is used to evolve chromosome subsets whose fitness is evaluated by a Logistic Regression classifier. The optimal identified features are then used to feed LDA for a CBIR system, based on a K-Nearest Neighbor classification. To assess the proposed approach, the authors have opted for a K-fold cross validation method on a database of 1097 images of melanomas and other skin lesions. As a result, the authors obtained a reduced number of features and an improved CBDIR system compared to PCA, LDA and ICA methods.

scholarly journals A novel content based image retrieval system using K-means/KNN with feature extraction

2012 ◽  
Vol 9 (4) ◽  
pp. 1645-1661 ◽  
Author(s):  
Ray-I Chang ◽  
Shu-Yu Lin ◽  
Jan-Ming Ho ◽  
Chi-Wen Fann ◽  
Yu-Chun Wang
Keyword(s):  
Feature Extraction ◽  
Image Retrieval ◽  
Nearest Neighbor ◽  
Clustering Algorithms ◽  
Content Based Image Retrieval ◽  
K Nearest Neighbor ◽  
Color Analysis ◽  
Image Retrieval System ◽  
First Time ◽  
System Designs

Image retrieval has been popular for several years. There are different system designs for content based image retrieval (CBIR) system. This paper propose a novel system architecture for CBIR system which combines techniques include content-based image and color analysis, as well as data mining techniques. To our best knowledge, this is the first time to propose segmentation and grid module, feature extraction module, K-means and k-nearest neighbor clustering algorithms and bring in the neighborhood module to build the CBIR system. Concept of neighborhood color analysis module which also recognizes the side of every grids of image is first contributed in this paper. The results show the CBIR systems performs well in the training and it also indicates there contains many interested issue to be optimized in the query stage of image retrieval.

A Novel Dynamic Hybridization Method for Best Feature Selection

2021 ◽  
Vol 12 (2) ◽  
pp. 85-99
Author(s):  
Nassima Dif ◽  
Zakaria Elberrichi
Keyword(s):  
Feature Selection ◽  
Nearest Neighbor ◽  
Optimization Problems ◽  
Learning Algorithm ◽  
Accuracy Score ◽  
Hybridization Method ◽  
K Nearest Neighbor ◽  
Feature Selection Problem ◽  
Combinatorial Optimization Problems ◽  
The Comparative Study

Hybrid metaheuristics has received a lot of attention lately to solve combinatorial optimization problems. The purpose of hybridization is to create a cooperation between metaheuristics for better solutions. Most proposed works were interested in static hybridization. The objective of this work is to propose a novel dynamic hybridization method (GPBD) that generates the most suitable sequential hybridization between GA, PSO, BAT, and DE metaheuristics, according to each problem. The authors choose to test this approach for solving the best feature selection problem in a wrapper tactic, performed on face image recognition datasets, with the k-nearest neighbor (KNN) learning algorithm. The comparative study of the metaheuristics and their hybridization GPBD shows that the proposed approach achieved the best results. It was definitely competitive with other filter approaches proposed in the literature. It achieved a perfect accuracy score of 100% for Orl10P, Pix10P, and PIE10P datasets.

A NOVEL INDEXING AND ACCESS MECHANISM USING AFFINITY HYBRID TREE FOR CONTENT-BASED IMAGE RETRIEVAL IN MULTIMEDIA DATABASES

2007 ◽  
Vol 01 (02) ◽  
pp. 147-170 ◽  
Author(s):  
KASTURI CHATTERJEE ◽  
SHU-CHING CHEN
Keyword(s):  
Image Retrieval ◽  
Metric Spaces ◽  
Nearest Neighbor ◽  
Human Perception ◽  
Multimedia Databases ◽  
Multimedia Retrieval ◽  
Index Structure ◽  
Content Based Image Retrieval ◽  
K Nearest Neighbor ◽  
Computational Overhead

An efficient access and indexing framework, called Affinity Hybrid Tree (AH-Tree), is proposed which combines feature and metric spaces in a novel way. The proposed framework helps to organize large image databases and support popular multimedia retrieval mechanisms like Content-Based Image Retrieval (CBIR). It is efficient in terms of computational overhead and fairly accurate in producing query results close to human perception. AH-Tree, by being able to introduce the high level semantic image relationship as it is in its index structure, solves the problem of translating the content-similarity measurement into feature level equivalence which is both painstaking and error-prone. Algorithms for similarity (range and k-nearest neighbor) queries are implemented and extensive experiments are performed which produces encouraging results with low I/O and distance computations and high precision of query results.

scholarly journals Product Review Based Customer Sentiment Analysis using an Ensemble of mRMR and Forest Optimization Algorithm (FOA)

2022 ◽  
Vol 13 (1) ◽  
pp. 0-0
Keyword(s):  
Feature Selection ◽  
Sentiment Analysis ◽  
Optimization Algorithm ◽  
Nearest Neighbor ◽  
Hybrid Approach ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Feature Selection Technique ◽  
Feature Selection Problem

This research presents a way of feature selection problem for classification of sentiments that use ensemble-based classifier. This includes a hybrid approach of minimum redundancy and maximum relevance (mRMR) technique and Forest Optimization Algorithm (FOA) (i.e. mRMR-FOA) based feature selection. Before applying the FOA on sentiment analysis, it has been used as feature selection technique applied on 10 different classification datasets publically available on UCI machine learning repository. The classifiers for example k-Nearest Neighbor (k-NN), Support Vector Machine (SVM) and Naïve Bayes used the ensemble based algorithm for available datasets. The mRMR-FOA uses the Blitzer’s dataset (customer reviews on electronic products survey) to select the significant features. The classification of sentiments has noticed to improve by 12 to 18%. The evaluated results are further enhanced by the ensemble of k-NN, NB and SVM with an accuracy of 88.47% for the classification of sentiment analysis task.

scholarly journals Logistic Regression based Feature Selection and Two-Stage Detection for EEG based Motor Imagery Classification

2021 ◽  
Vol 14 (1) ◽  
pp. 134-146
Author(s):  
Adi Wijaya ◽  
◽  
Teguh Adji ◽  
Noor Setiawan ◽  
◽  
...  
Keyword(s):  
Logistic Regression ◽  
Feature Selection ◽  
Motor Imagery ◽  
Nearest Neighbor ◽  
K Nearest Neighbor ◽  
Two Stage ◽  
Selection Scheme ◽  
Linear Discriminant ◽  
Second Stage ◽  
Electroencephalogram Eeg

Electroencephalogram (EEG) based motor imagery (MI) classification requires efficient feature extraction and consistent accuracy for reliable brain-computer interface (BCI) systems. Achieving consistent accuracy in EEGMI classification is still big challenge according to the nature of EEG signal which is subject dependent. To address this problem, we propose a feature selection scheme based on Logistic Regression (LRFS) and two-stage detection (TSD) in channel instantiation approach. In TSD scheme, Linear Discriminant Analysis was utilized in first-stage detection; while Gradient Boosted Tree and k-Nearest Neighbor in second-stage detection. To evaluate the proposed method, two publicly available datasets, BCI competition III-Dataset IVa and BCI competition IV-Dataset 2a, were used. Experimental results show that the proposed method yielded excellent accuracy for both datasets with 95.21% and 94.83%, respectively. These results indicated that the proposed method has consistent accuracy and is promising for reliable BCI systems.

scholarly journals Preprocessing of Skin Images and Feature Selection for Early Stage of Melanoma Detection using Color Feature Extraction

2021 ◽  
Vol 4 (2) ◽  
pp. 95
Author(s):  
Yuita Arum Sari ◽  
Anggi Gustiningsih Hapsani ◽  
Sigit Adinugroho ◽  
Lukman Hakim ◽  
Siti Mutrofin
Keyword(s):  
Feature Extraction ◽  
Feature Selection ◽  
Nearest Neighbor ◽  
Early Stage ◽  
Normal Skin ◽  
Feature Selection Method ◽  
K Nearest Neighbor ◽  
Color Feature ◽  
K Value ◽  
Linear Discriminant

Preprocessing is an essential part to achieve good segmentation since it affects the feature extraction process. Melanoma have various shapes and their extracted features from image are used for early stage detection. Due to the fact that melanoma is one of dangerous diseases, early detection is required to prevent further phase of cancer from developing. In this paper, we propose a new framework to detect cancer on skin images using color feature extraction and feature selection. The default color space of skin images is RGB, then brightness is added to distinguish the normal and darken area on the skin. After that, average filter and histogram equalization are applied as well for attaining a good color intensities which are capable of determining normal skin from suspicious one. Otsu thresholding is utilized afterwards for melanoma segmentation. There are 147 features extracted from segmented images. Those features are reduced using three types of feature selection algorithms: Linear Discriminant Analysis (LDA), Correlation based Feature Selection (CFS), and Relief. All selected features are classified using k-Nearest Neighbor  (k-NN). Relief is known to be the best feature selection method among others and the optimal k value is 7 with 10-cross validation with accuracy of 0.835 and 0.845, without and with feature selection respectively. The result indicates that the frameworks is applicable for early skin cancer detection.

scholarly journals K-Nearest Neighbor Based Enhanced-CBIR System

2020 ◽  
Vol 9 (4) ◽  
pp. 550-553
Keyword(s):  
Image Retrieval ◽  
Nearest Neighbor ◽  
Directional Data ◽  
Content Based Image Retrieval ◽  
K Nearest Neighbor

In content based image retrieval is the most widely recognized feature utilized are shape, hues, surface and so on. To improve the exactness of retrieval, it must look on the far side the old style features. The features which could without much of a stretch be extracted from information could be considered. One of such feature is directionality of the picture surface. Directional data can be spoken to in a minimized way by utilizing transform like wavelet, Gabor, Radon and so on. In this proposal, we address this issue of utilizing directional data to build exactness of enhanced-CBIR. Picture retrieval execution is assessed by utilizing Precession and Recall. These calculations are most appropriate for retrieval of textural pictures. Our proposed Enhanced-CBIR system which works combine with KNN algorithm, provides better quality of result compare than the existing CBIR framework.

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