Image Segmentation Approach Based on Hybridization Between K-Means and Mask R-CNN

Moussaoui, Hanae; Benslimane, Mohamed; Akkad, Nabil El

doi:10.1007/978-981-33-6893-4_74

Cited by 16 publications

(10 citation statements)

References 18 publications

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“…For instance, figure 5 shows the three rows, where input image in first row, ground truth image in second row and finally, segmented images using proposed model in third row. [22,27], RNN [39], CNN [41], GAN [42], YOLO [26,27], CSO [31] and SSA [31] are all tested with these two datasets and results are mentioned in the following tables.…”

Section: Segmentation Analysismentioning

confidence: 99%

YOLO Based Deep Learning Model for Segmenting the Color Images

Rasi¹,

AntoBennet

Renjith

et al. 2023

IJEER

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The first stage is to extract fine details from a picture using Red Green Blue (RGB) colour space is colour image segmentation. Most grayscale and colour picture segmentation algorithms use original or updated fuzzy c-means (FCM) clustering. However, due to two factors, the majority of these methods are inefficient and fail to produce the acceptable segmentation results for colour photos. The inclusion of local spatial information often results in a high level of computational complexity due to the repetitive distance computation between clustering centres and pixels within a tiny adjacent window. The second reason is that a typical neighbouring window tends to mess up the local spatial structure of images. Color picture segmentation has been improved by introducing Deep Convolution Neural Networks (CNNs) for object detection, classification and semantic segmentation. This study seeks to build a light-weight for object detector that uses a depth and colour image from a publically available dataset to identify objects in a scene. It's likely to output in the depth way by expanding the YOLO network's network architecture. Using Taylor based Cat Salp Swarm algorithm (TCSSA), the weight of the suggested model is modified to improve the accuracy of region extraction findings. It is possible to test the detector's efficacy by comparing it to various datasets. Testing showed that the suggested model is capable of segmenting input into multiple metrics using bounding boxes. The results shows that the proposed model achieved 0.20 of Global Consistency Error (GCE) and 1.85 of Variation of Information (VOI) on BSDS500 dataset, where existing techniques achieved nearly 1.96 to 1.86 of VOI and 0.25 to 0.22 of GCE for the same dataset.

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Section: Segmentation Analysismentioning

confidence: 99%

YOLO Based Deep Learning Model for Segmenting the Color Images

Rasi¹,

AntoBennet

Renjith

et al. 2023

IJEER

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show abstract

“…Furthermore, reinforcement and deep reinforcement learning might be included in medical imaging as well ( [83], [84]) for object and lesion detection, surgical image segmentation, and the classification of different medical images. while image segmentation ( [85], [86], [87], [88], [89], [90]) is considered a challenging task, first is the fact for obtaining pixel-wise is very costly, secondly, is that the real world segmentation data is highly imbalanced which biases the performance towards the most represented categories. Consequently, it's required to minimize human labeling effort and maximize the segmentation performance simultaneously.…”

Section: A Reinforcement Learning Algorithmsmentioning

confidence: 99%

Reinforcement Learning: A review

Moussaoui¹,

Akkad²,

Benslimane³

2023

IJCDS

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Reinforcement learning is considered a sort of machine learning that acquires knowledge of solving problems using the trial-and-error technique. The process starts with the main actor that is the agent interacting with a given environment and attempting to achieve a multi-step goal within this environment. The environment is characterized by a state that the agent detects and examines. On the other hand, due to the agent's several actions, the environment's state changes according to these modifications. Eventually, and at this stage, the agent gets reward signals as it proceeds nearer to its goal. The agent uses these rewards signals to determine which actions were successful and which actions were not. The state action is then repeated and the reward is looped until the agent learns how to operate effectively within the environment using the trial-and-error concept. The agent's main objective is to learn how to always choose the right action given any state of the environment that leads it closer to its goal. In this paper, we gathered all the methods used in the literature. Multi-armed bandits, the Markov decision process, Monte Carlo methods, dynamic programming as well as temporal-difference learning are some of the corresponding methods used to solve reinforcement learning issues. The current paper is organized and structured as follows: we'll start with an introduction followed by a reinforcement learning section where we discussed all the methods and techniques used in the literature. Furthermore, the third section will be about deep reinforcement learning, here we gathered deep reinforcement learning techniques. In the fourth section, we will summarize the reinforcement and deep reinforcement learning algorithms in detail. Furthermore, we will finalize the article with a discussion and a conclusion.

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“…Still, they are unsuitable for image encryption because their security is principally focused on high computational cost, whereas images are distinguished by massive data capacity and good correlation between adjacent pixels. Therefore, all researchers became interested in developing an efficient, effective, and secure image encryption method [36,37,38,6,7].…”

Section: Introductionmentioning

confidence: 99%

Securing Color Images with an Innovative Hybrid Method Combining DNA Computing and Chaotic Systems

Mansouri,

El ghouate,

Tahiri

et al. 2024

Stat., optim. inf. comput.

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Modern cryptography is a key element of data security, ensuring the confidentiality and integrity of information. In an increasingly digital world, cryptography remains crucial for the protection of sensitive data. In this context, we propose a novel hybrid security system for encrypted color images using a DNA model, chaotic systems, and SHA256-MD5 hash functions as a basis. The proposed hybrid system includes DNA permutation and diffusion. In DNA permutation, we unpredictably rearrange the location of DNA image elements by using the logistic map of low computational complexity. In DNA diffusion, we diffuse the permuted image of DNA with the DNA image key generated by a 5D hyper-chaotic system, using a variety of algebraic operators such as the circular offset in both directions. Considering the experimental outcomes and security evaluation, we can infer that the proposed hybrid security system demonstrates a high level of security, resistance to existing attacks, and practical application suitability while maintaining speed.

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Image Segmentation Approach Based on Hybridization Between K-Means and Mask R-CNN

Cited by 16 publications

References 18 publications

YOLO Based Deep Learning Model for Segmenting the Color Images

YOLO Based Deep Learning Model for Segmenting the Color Images

Reinforcement Learning: A review

Securing Color Images with an Innovative Hybrid Method Combining DNA Computing and Chaotic Systems

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