Restoration of Archaeological Artifacts by a Genetic Algorithm with Image Features

2014 ◽  
Keyword(s):  
Genetic Algorithm ◽  
Image Features ◽  
Archaeological Artifacts

Image Dimensionality Reduction Based on the Intrinsic Dimension and Parallel Genetic Algorithm

2011 ◽  
Vol 5 (2) ◽  
pp. 97-112 ◽  
Author(s):  
Liang Lei ◽  
TongQing Wang ◽  
Jun Peng ◽  
Bo Yang
Keyword(s):  
Genetic Algorithm ◽  
Dimensionality Reduction ◽  
Image Features ◽  
Similarity Function ◽  
Nonlinear Dimensionality Reduction ◽  
General Covariance ◽  
Parallel Genetic Algorithm ◽  
Web Content ◽  
Genetic Operators ◽  
Intrinsic Dimension

In the research of Web content-based image retrieval, how to reduce more of the image dimensions without losing the main features of the image is highlighted. Many features of dimensional reduction schemes are determined by the breaking of higher dimensional general covariance associated with the selection of a particular subset of coordinates. This paper starts with analysis of commonly used methods for the dimension reduction of Web images, followed by a new algorithm for nonlinear dimensionality reduction based on the HSV image features. The approach obtains intrinsic dimension estimation by similarity calculation of two images. Finally, some improvements were made on the Parallel Genetic Algorithm (APGA) by use of the image similarity function as the self-adaptive judgment function to improve the genetic operators, thus achieving a Web image dimensionality reduction and similarity retrieval. Experimental results illustrate the validity of the algorithm.

scholarly journals Diffusion Parameters Analysis in a Content-Based Image Retrieval Task for Mobile Vision

Sensors ◽  
2020 ◽  
Vol 20 (16) ◽  
pp. 4449
Author(s):  
Federico Magliani ◽  
Laura Sani ◽  
Stefano Cagnoni ◽  
Andrea Prati
Keyword(s):  
Genetic Algorithm ◽  
Image Retrieval ◽  
Image Features ◽  
Retrieval Performance ◽  
Retrieval Task ◽  
K Nearest Neighbors ◽  
Diffusion Parameters ◽  
Recent Computer ◽  
Mobile Vision ◽  
And Performance

Most recent computer vision tasks take into account the distribution of image features to obtain more powerful models and better performance. One of the most commonly used techniques to this purpose is the diffusion algorithm, which fuses manifold data and k-Nearest Neighbors (kNN) graphs. In this paper, we describe how we optimized diffusion in an image retrieval task aimed at mobile vision applications, in order to obtain a good trade-off between computation load and performance. From a computational efficiency viewpoint, the high complexity of the exhaustive creation of a full kNN graph for a large database renders such a process unfeasible on mobile devices. From a retrieval performance viewpoint, the diffusion parameters are strongly task-dependent and affect significantly the algorithm performance. In the method we describe herein, we tackle the first issue by using approximate algorithms in building the kNN tree. The main contribution of this work is the optimization of diffusion parameters using a genetic algorithm (GA), which allows us to guarantee high retrieval performance in spite of such a simplification. The results we have obtained confirm that the global search for the optimal diffusion parameters performed by a genetic algorithm is equivalent to a massive analysis of the diffusion parameter space for which an exhaustive search would be totally unfeasible. We show that even a grid search could often be less efficient (and effective) than the GA, i.e., that the genetic algorithm most often produces better diffusion settings when equal computing resources are available to the two approaches. Our method has been tested on several publicly-available datasets: Oxford5k, ROxford5k, Paris6k, RParis6k, and Oxford105k, and compared to other mainstream approaches.

Automatic Feature Weight Assignment Based on Image Retrieval Using Genetic Algorithm

2014 ◽  
Vol 931-932 ◽  
pp. 1402-1406 ◽  
Author(s):  
Purichaya Srisook ◽  
Kata Praditwong
Keyword(s):  
Genetic Algorithm ◽  
Image Retrieval ◽  
Texture Features ◽  
Image Features ◽  
New Method ◽  
Content Based Image Retrieval ◽  
Equal Weight ◽  
Optimal Weight ◽  
Optimal Value ◽  
Feature Weight

The work proposes the new method to increase an efficiency of a Content-based Image Retrieval (CBIR) system. For combining many image features, the optimal weight of each feature is required. To find the optimal value of the feature, this work uses Genetic Algorithm (GA). An image is represented as color, shape and texture features. The experiment compares the results from the system with equal weight values and the system with the weights provided by GA. Evaluation shows the robustness and efficiency of the proposed technique.

Image Dimensionality Reduction Based on the Intrinsic Dimension and Parallel Genetic Algorithm

2012 ◽  
pp. 323-336
Author(s):  
Liang Lei ◽  
TongQing Wang ◽  
Jun Peng ◽  
Bo Yang
Keyword(s):  
Genetic Algorithm ◽  
Dimensionality Reduction ◽  
Image Features ◽  
Similarity Function ◽  
General Covariance ◽  
Parallel Genetic Algorithm ◽  
Web Content ◽  
Genetic Operators ◽  
Intrinsic Dimension ◽  
Higher Dimensional

In the research of Web content-based image retrieval, how to reduce more of the image dimensions without losing the main features of the image is highlighted. Many features of dimensional reduction schemes are determined by the breaking of higher dimensional general covariance associated with the selection of a particular subset of coordinates. This paper starts with analysis of commonly used methods for the dimension reduction of Web images, followed by a new algorithm for nonlinear dimensionality reduction based on the HSV image features. The approach obtains intrinsic dimension estimation by similarity calculation of two images. Finally, some improvements were made on the Parallel Genetic Algorithm (APGA) by use of the image similarity function as the self-adaptive judgment function to improve the genetic operators, thus achieving a Web image dimensionality reduction and similarity retrieval. Experimental results illustrate the validity of the algorithm.

A ROUGHNESS MEASUREMENT METHOD BASED ON GENETIC ALGORITHM AND NEURAL NETWORK FOR MICROHETEROGENEOUS SURFACE IN DEEP-HOLE PARTS

2012 ◽  
Vol 21 (01) ◽  
pp. 1250005 ◽  
Author(s):  
WEI LIU ◽  
XIUYAN ZHENG ◽  
SHUANGJUN LIU ◽  
ZHENYUAN JIA
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Surface Roughness ◽  
Hybrid Model ◽  
Measurement Method ◽  
Image Features ◽  
Training Methods ◽  
Microscopic Image ◽  
Deep Hole ◽  
Feed Forward Back Propagation

It is difficult to measure the surface roughness of microheterogeneous surface in deep-hole parts due to the limitation of measurement space. In this paper, we propose a new method based on microscopic vision to detect the surface roughness of R-surface in the valve. First, the clear microscopic image of R-surface is obtained by the established microscopic system, which is mainly fabricated by the long working distance lenses of digital microscopic camera. Thereafter, based on genetic algorithm (GA) and feed-forward back propagation artificial neural network (BP-ANN), a hybrid method is proposed to predict the surface roughness. In this method, the microscopic image features of R-surface are taken as the inputs to the hybrid model. GA is employed to search the optimal initial weights and thresholds of BP-ANN, which resolves the problem that training methods of BP-ANN are much sensitive to initial weight values and thresholds. In addition, in virtue of a three-dimensional surface profiler, the targets of hybrid model are calibrated by the actual roughness values of R-surface in the sample valves, where the sections of sample valves over R-surface are cut. Finally, experiments on the microscopic image acquisition and roughness calibration are conducted, as well as the prediction experiments. Moreover, the analysis results indicate that the proposed measurement method based on GA and BP-ANN exhibits high precision and stability for evaluating the microcosmic surface roughness of microheterogeneous surface in deep-hole parts.

Digital Steganography Based on Genetic Algorithm

2011 ◽  
pp. 439-453
Author(s):  
Frank Y. Shih ◽  
Yi-Ta Wu
Keyword(s):  
Genetic Algorithm ◽  
Image Features ◽  
Secret Data ◽  
Hidden Data ◽  
Digital Steganography

Steganography is the art of hiding secret data inside other innocent media file. Steganalysis is the process of detecting hidden data which are crested using steganography. Steganalysis detects stego-images by analyzing various image features between stego-images and cover-images. Therefore, we need to have a system that develops more critical stego-images from which steganalysis cannot detect them. In this chapter, we present a Genetic algorithm-(GA) based method for breaking steganalytic systems. The emphasis is shifted from traditionally avoiding the change of statistic features to artificially counterfeiting the statistic features. Our idea is based on the following: in order to manipulate the statistic features for breaking the inspection of steganalytic systems, the GA-based approach is adopted to counterfeit several stego-images (candidates) until one of them can break the inspection of steganalytic systems.

scholarly journals Survival prediction for oral tongue cancer patients via probabilistic genetic algorithm optimized neural network models

2020 ◽  
Vol 93 (1112) ◽  
pp. 20190825
Author(s):  
Xiaoying Pan ◽  
Ting Zhang ◽  
QingPing Yang ◽  
Di Yang ◽  
Jean-Claude Rwigema ◽  
...  
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Patient Survival ◽  
Back Propagation ◽  
Network Models ◽  
Image Features ◽  
Survival Prediction ◽  
Reduction Algorithm ◽  
Neural Network Models ◽  
Pre Treatment

Objectives: High throughput pre-treatment imaging features may predict radiation treatment outcome and guide individualized treatment in radiotherapy (RT). Given relatively small patient sample (as compared with high dimensional imaging features), identifying potential prognostic imaging biomarkers is typically challenging. We aimed to develop robust machine learning methods for patient survival prediction using pre-treatment quantitative CT image features for a subgroup of head-and-neck cancer patients. Methods: Three neural network models, including back propagation (BP), Genetic Algorithm-Back Propagation (GA-BP), and Probabilistic Genetic Algorithm-Back Propagation (PGA-BP) neural networks were trained to simulate association between patient survival and radiomics data in radiotherapy. To evaluate the models, a subgroup of 59 head-and-neck patients with primary cancers in oral tongue area were utilized. Quantitative image features were extracted from planning CT images, a novel t-Distributed Stochastic Neighbor Embedding (t-SNE) method was used to remove irrelevant and redundant image features before fed into the network models. 80% patients were used to train the models, and remaining 20% were used for evaluation. Results: Of the three supervised machine-learning methods studied, PGA-BP yielded the best predictive performance. The reported actual patient survival interval of 30.5 ± 21.3 months, the predicted survival times were 47.3 ± 38.8, 38.5 ± 13.5 and 29.9 ± 15.3 months using the traditional PCA. Combining with the novel t-SNE dimensionality reduction algorithm, the predicted survival intervals are 35.8 ± 15.2, 32.3 ± 13.1 and 31.6 ± 15.8 months for the BP, GA-BP and PGA-BP neural network models, respectively. Conclusion: The work demonstrated that the proposed probabilistic genetic algorithm optimized neural network models, integrating with the t-SNE dimensionality reduction algorithm, achieved accurate prediction of patient survival. Advances in knowledge: The proposed PGA-BP neural network, integrating with an advanced dimensionality reduction algorithm (t-SNE), improved patient survival prediction accuracy using pre-treatment quantitative CT image features of head-and-neck cancer patients.

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