Special Issue "Bio-Inspired Algorithms for Image Processing"
Deadline for manuscript submissions: 30 June 2020.
Interests: GPGPU; CUDA; bio-inspired algorithms; image processing; parallel algorithms
Interests: machine learning; deep neural networks; parallel programming
In the field of image processing, there are several hard problems without exact solutions due to incomplete or imperfect information and limited computation capacity. Some methods of the different subfields (classification, feature extraction, pattern recognition, segmentation, etc.) are based on the process of natural selection, the behavior of living creatures, or especially on the mechanisms of the brain. It is also worth mentioning that in the case of traditional procedural image processing methods, where the algorithms are well-defined, the parametrization can be challenging, if not impossible. This step can be formalized as an optimization problem, where the application of heuristics is necessary for addressing such highly complex problems to provide feasible solutions in acceptable runtime. Many of these optimization techniques are also inspired by nature.
In this Special Issue of "Bio-inspired Algorithms for Image Processing", we seek original research or results of practical applications from the area of bio-inspired algorithms in the field of image processing. We are waiting for manuscripts discussing evolutional (genetic algorithms, NSGA, etc.), swarm intelligence-based (particle swarm optimization, ant colony optimization, fireworks, etc.) and brain-inspired computing (neural networks, deep learning, etc.) methods applied in any kind of image processing research projects (classification, segmentation, medical image processing, etc.).
Dr. Sandor Szenasi
Dr. Gábor Kertész
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 papers will be 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. Algorithms 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 1000 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.
- Design and analysis of bio-inspired methods
- Application of bio-inspired methods
- Limitations of bio-inspired methods
- Ant colony optimization
- Particle swarm optimization
- Firefly algorithm
- Fireworks algorithm
- Bees algorithm
- Evolutionary algorithms
- Neural networks
- Deep learning
- Soft computing methods
- Nature-inspired heuristics
The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.
Title: Metric Embedding Learning on Multi-Directional Projections
Abstract: Image based instance recognition is a difficult problem, in some cases even for the human eye. While latest developments in computer vision -- mostly driven by deep learning -- have shown that high performance models for classification or categorization can be engineered, the problem of discriminating similar objects with a low number of samples remain challenging. Advances from multi-class classification are applied for object matching problems, as the feature extraction techniques are the same: nature-inspired multi-layered convolutional nets learn the representations from images, and the output of such a model maps them to a multidimensional encoding space. A metric based loss is defined, bringing embeddings from same instances close to each other. While these solutions achieve high classification performance, low efficiency is caused by memory cost of parameter number, which is in a relationship with input image size; on shrinking the input, the model require less trainable parameters, while performance decreases. This drawback is tackled by using compressed representations of images, e.g. image projections. In this paper, a multi-directional image projection transformation resulting in fixed vector lengths (MDIPFL) is applied for image one-shot recognition tasks, trained on Siamese and Triplet architectures.
Keywords: deep metric learning, one-shot learning, multi-directional image projections, object matching, object re-identification