Image segmentation by minimum cross entropy using evolutionary methods

Oliva, Diego; Hinojosa, Salvador; Osuna-Enciso, Valentín; Cuevas, Erik; Pérez‐Cisneros, Marco; Sánchez-Ante, Gildardo

doi:10.1007/s00500-017-2794-1

Cited by 53 publications

(18 citation statements)

References 46 publications

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“…Assume and , which represent two probability distributions. The cross entropy between and can be computed using Equation 2 (Horng 2010; Oliva et al, 2019).…”

Section: Cross Entropy Methodsmentioning

confidence: 99%

A Multi-objective Invasive Weed Optimization Method for Segmentation of Distress Images

Abdelkader

Moselhi²,

Marzouk

et al. 2020

Intelligent Automation &Amp; Soft Computing

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“…Assume and , which represent two probability distributions. The cross entropy between and can be computed using Equation 2 (Horng 2010; Oliva et al, 2019).…”

Section: Cross Entropy Methodsmentioning

confidence: 99%

A Multi-objective Invasive Weed Optimization Method for Segmentation of Distress Images

Abdelkader

Moselhi²,

Marzouk

et al. 2020

Intelligent Automation &Amp; Soft Computing

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“…Image entropy is a statistical form of image features, which reflects the average amount of information in an image [28][29]. The one-dimensional entropy of the image represents the information contained in the aggregation feature of the gray distribution in the image.…”

Section: Figure 4 Algorithm Structure Diagrammentioning

confidence: 99%

Video Key Frame Monitoring Algorithm and Virtual Reality Display Based on Motion Vector

Wang

Zhu

2020

IEEE Access

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In this paper, a motion vector-based video key frame detection algorithm is proposed to solve the problem of miss election and missing selection caused by the difficulty in detecting the moving target characteristics of the video key frame. Firstly, the entropy of adjacent frame difference and the twodimensional entropy of image are introduced, and the combination of the two is taken as the measurement of the difference between video frames. Secondly, outliers are detected by statistical tools to obtain the lens boundary, thus realizing the adaptive lens detection of video content. Then, ViBe algorithm is used to detect the foreground object in the video sequence and extract the scale-invariant feature transformation features of the foreground moving object. Finally, the motion vector is introduced, and the sum of the block matching results is motion vector by partitioning the two adjacent frames and performing block matching. The magnitude of motion vector reflects the intensity of motion in the video, so active and inactive motion regions are obtained, and the similarity of video frames is calculated according to the defined formula, and key frames are extracted in these regions respectively. The experimental results show that the detection algorithm proposed in this paper improves the video results with rich motion information obviously, and the objective indexes and subjective scores are improved to some extent, which improves the universality of the algorithm. In addition, this paper also studies the display mode of key frame extraction results in virtual reality environment. The key frame display mode of video in virtual reality is optimized mainly by changing the display mode of information and changing the scene and testing the result of user task execution.INDEX TERMS Video; key Frame; monitoring algorithm; virtual reality display; motion vector.

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“…Shannon's entropy is very popular in the field of communications. Many researchers have extended the concept of entropy for image processing purposes [47].…”

Section: Problem Statement and Proposed Algorithmmentioning

confidence: 99%

Entropy-Based Convex Set Optimization for Spatial–Spectral Endmember Extraction From Hyperspectral Images

Shah

Zaveri

Trivedi

2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Spectral unmixing is an important problem for remotely sensed hyperspectral data exploitation. Automatic spectral unmixing can be viewed as a three-stage problem, where the first stage is subspace identification, the next one is endmember extraction, and the final one is abundance estimation. In this sequence, endmember extraction is the most challenging problem. Many researchers have attempted to extract endmembers from hyperspectral images using spectral information only. However, it is well known that the inclusion of spatial information can improve the endmember extraction task. In this paper, we introduce a new endmember extraction algorithm that exploits both spectral and spatial information. A main innovation of the proposed algorithm is that spatial information is exploited using entropy, while spectral information is exploited using convex set optimization. In the literature, none of the spatial-spectral algorithms has used entropy as spatial information. The inclusion of this entropy-based spatial information improves the accuracy of the endmember extraction process. The results obtained by the proposed algorithm are compared (using a variety of metrics) with those obtained by other state-of-the-art methods, using both synthetic and real datasets. Our experimental results demonstrate that the proposed algorithm outperforms many available algorithms.

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Image segmentation by minimum cross entropy using evolutionary methods

Cited by 53 publications

References 46 publications

A Multi-objective Invasive Weed Optimization Method for Segmentation of Distress Images

A Multi-objective Invasive Weed Optimization Method for Segmentation of Distress Images

Video Key Frame Monitoring Algorithm and Virtual Reality Display Based on Motion Vector

Entropy-Based Convex Set Optimization for Spatial–Spectral Endmember Extraction From Hyperspectral Images

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