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J. Imaging
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Color Image Segmentation...
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Color Image Segmentation

A special issue of Journal of Imaging (ISSN 2313-433X).

Deadline for manuscript submissions: closed (31 May 2020) | Viewed by 12323

Special Issue Editor

Prof. Dr. Mihai Ivanovici

E-Mail Website
Guest Editor
Faculty of Electrical Engineering and Computer Science, Transilvania University of Brasov , Politehnicii 1, 500024 Brasov, Romania
Interests: color features; color texture; color and complexity perception; multispectral and hyperspectral image segmentation; big data; deep learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

There exists a multitude of image segmentation approaches, spreading from watershed or pyramidal approaches to graph-based or active contours. The segmentation techniques evolved from the classical single-stage open-loop ones to semantic, supervised, or multi-level closed-loops, for the optimization of specific criteria. When designing an image segmentation approach, many parameters have to be considered: neighborhood, homogeneity, distance, similarity, features, texture, etc. However, color drives the need for integrating other aspects from fields like colorimetry, human perception, or cognitive science. Moreover, due to the rapid increase of spatial and spectral resolution of imaging sensors, color image segmentation should consider aspects from big data, multi or hyperspectral imaging, or remote sensing domains. Last but not least, following the current trends in the artificial intelligence domain, color image segmentation definitely should mean more than performing a classification of image features.

Prof. Dr. Mihai Ivanovici
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Imaging is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Color features and color texture characterization
  • Color and complexity perception
  • Multispectral and hyperspectral image segmentation
  • Big data and deep learning.

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Published Papers (3 papers)

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Research

19 pages, 14069 KiB  
Article
Edge-Based Color Image Segmentation Using Particle Motion in a Vector Image Field Derived from Local Color Distance Images
by Wutthichai Phornphatcharaphong and Nawapak Eua-Anant
J. Imaging 2020, 6(7), 72; https://doi.org/10.3390/jimaging6070072 - 16 Jul 2020
Cited by 10 | Viewed by 3680
Abstract
This paper presents an edge-based color image segmentation approach, derived from the method of particle motion in a vector image field, which could previously be applied only to monochrome images. Rather than using an edge vector field derived from a gradient vector field [...] Read more.
This paper presents an edge-based color image segmentation approach, derived from the method of particle motion in a vector image field, which could previously be applied only to monochrome images. Rather than using an edge vector field derived from a gradient vector field and a normal compressive vector field derived from a Laplacian-gradient vector field, two novel orthogonal vector fields were directly computed from a color image, one parallel and another orthogonal to the edges. These were then used in the model to force a particle to move along the object edges. The normal compressive vector field is created from the collection of the center-to-centroid vectors of local color distance images. The edge vector field is later derived from the normal compressive vector field so as to obtain a vector field analogous to a Hamiltonian gradient vector field. Using the PASCAL Visual Object Classes Challenge 2012 (VOC2012), the Berkeley Segmentation Data Set, and Benchmarks 500 (BSDS500), the benchmark score of the proposed method is provided in comparison to those of the traditional particle motion in a vector image field (PMVIF), Watershed, simple linear iterative clustering (SLIC), K-means, mean shift, and J-value segmentation (JSEG). The proposed method yields better Rand index (RI), global consistency error (GCE), normalized variation of information (NVI), boundary displacement error (BDE), Dice coefficients, faster computation time, and noise resistance. Full article
(This article belongs to the Special Issue Color Image Segmentation )
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14 pages, 2616 KiB  
Article
A Discriminative Long Short Term Memory Network with Metric Learning Applied to Multispectral Time Series Classification
by Merve Bozo, Erchan Aptoula and Zehra Çataltepe
J. Imaging 2020, 6(7), 68; https://doi.org/10.3390/jimaging6070068 - 12 Jul 2020
Cited by 8 | Viewed by 3618
Abstract
In this article, we propose an end-to-end deep network for the classification of multi-spectral time series and apply them to crop type mapping. Long short-term memory networks (LSTMs) are well established in this regard, thanks to their capacity to capture both long and [...] Read more.
In this article, we propose an end-to-end deep network for the classification of multi-spectral time series and apply them to crop type mapping. Long short-term memory networks (LSTMs) are well established in this regard, thanks to their capacity to capture both long and short term temporal dependencies. Nevertheless, dealing with high intra-class variance and inter-class similarity still remain significant challenges. To address these issues, we propose a straightforward approach where LSTMs are combined with metric learning. The proposed architecture accommodates three distinct branches with shared weights, each containing a LSTM module, that are merged through a triplet loss. It thus not only minimizes classification error, but enforces the sub-networks to produce more discriminative deep features. It is validated via Breizhcrops, a very recently introduced and challenging time series dataset for crop type mapping. Full article
(This article belongs to the Special Issue Color Image Segmentation )
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15 pages, 2858 KiB  
Article
Color Image Complexity versus Over-Segmentation: A Preliminary Study on the Correlation between Complexity Measures and Number of Segments
by Mihai Ivanovici, Radu-Mihai Coliban, Cosmin Hatfaludi and Irina Emilia Nicolae
J. Imaging 2020, 6(4), 16; https://doi.org/10.3390/jimaging6040016 - 30 Mar 2020
Cited by 6 | Viewed by 4265
Abstract
It is said that image segmentation is a very difficult or complex task. First of all, we emphasize the subtle difference between the notions of difficulty and complexity. Then, in this article, we focus on the question of how two widely used color [...] Read more.
It is said that image segmentation is a very difficult or complex task. First of all, we emphasize the subtle difference between the notions of difficulty and complexity. Then, in this article, we focus on the question of how two widely used color image complexity measures correlate with the number of segments resulting in over-segmentation. We study the evolution of both the image complexity measures and number of segments as the image complexity is gradually decreased by means of low-pass filtering. In this way, we tackle the possibility of predicting the difficulty of color image segmentation based on image complexity measures. We analyze the complexity of images from the point of view of color entropy and color fractal dimension and for color fractal images and the Berkeley data set we correlate these two metrics with the segmentation results, more specifically the number of quasi-flat zones and the number of JSEG regions in the resulting segmentation map. We report on our experimental results and draw conclusions. Full article
(This article belongs to the Special Issue Color Image Segmentation )
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