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Advances in Object-Based Image Segmentation and Retrieval
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Advances in Object-Based Image Segmentation and Retrieval

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: closed (31 May 2023) | Viewed by 10497

Special Issue Editors

Prof. Dr. Chuen-Horng Lin

E-Mail Website
Guest Editor
Department of Computer Science and Information Engineering, National Taichung University of Science and Technology, Taichung 40401, Taiwan
Interests: computer vision; image processing; machine learning and deep learning; pattern recognition; automated image and video analysis through detection; tracking and segmentation methods
Prof. Dr. Yung-Kuan Chan

E-Mail Website
Guest Editor
Department of Management Information Systems, National Chung Hsing University, Taichung 40227, Taiwan
Interests: image processing; medical imaging technology; data exploration; management mathematics; database management systems;, algorithms; advanced image processing; STEM education
Special Issues, Collections and Topics in MDPI journals
Dr. Kuan-Hsien Liu

E-Mail Website
Guest Editor
Department of Computer Science and Information Engineering, National Taichung University of Science and Technology, Taichung 40401, Taiwan
Interests: signal and image processing; big data analytics; computer vision; machine learning and deep learning; facial age estimation/ facial expression recognition
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The MDPI journal Information invites submissions to a Special Issue on “Advances in Object-Based Image Segmentation and Retrieval”.

Contributors are invited to submit and present papers in various areas, from concepts to applications. The topics of interest include the latest research results and perspectives for future work in object-based image segmentation and retrieval.

Specifically, this Special Issue seeks novel research reports on the spectrum of AI’s influence on object-based image segmentation and retrieval. The editors welcome submissions on all forms of AI approaches, emphasizing applications of these approaches in real-world settings with thoroughly analyzed research results. This Special Issue aims to offer a platform for researchers from academia and industry to publish recent research findings and discuss opportunities, challenges, and solutions related to the segmentation and retrieval of objects. In particular, this Special Issue solicits original research papers about state-of-the-art approaches, methodologies, insights, and technologies enabling efficiency, theories, and practical applications toward the segmentation and retrieval of objects.

Topics of interest include (but are not limited to):

  • Content-based image retrieval
  • Detection, recognition, and classification
  • Document recognition
  • Face detection and recognition
  • Image and video retrieval
  • Image and video analysis and segmentation
  • Image and video labeling and retrieval
  • Image segmentation
  • Image and video retrieval
  • Object segmentation
  • Object detection and recognition
  • Object and scene recognition
  • Object detection, recognition, and categorization
  • Object detection and tracking in video
  • Pattern analysis and classification
  • Pattern recognition and analysis
  • Pattern recognition in new modalities

Prof. Dr. Chuen-Horng Lin
Prof. Dr. Yung-Kuan Chan
Dr. Kuan-Hsien Liu
Guest Editors

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. Information 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 1600 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

  • deep learning
  • object detection
  • object recognition
  • object segmentation
  • image segmentation
  • image retrieval

Published Papers (3 papers)

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Research

14 pages, 2220 KiB  
Article
Breast Cancer Detection in Mammography Images: A CNN-Based Approach with Feature Selection
by Zahra Jafari and Ebrahim Karami
Information 2023, 14(7), 410; https://doi.org/10.3390/info14070410 - 16 Jul 2023
Cited by 8 | Viewed by 6807
Abstract
The prompt and accurate diagnosis of breast lesions, including the distinction between cancer, non-cancer, and suspicious cancer, plays a crucial role in the prognosis of breast cancer. In this paper, we introduce a novel method based on feature extraction and reduction for the [...] Read more.
The prompt and accurate diagnosis of breast lesions, including the distinction between cancer, non-cancer, and suspicious cancer, plays a crucial role in the prognosis of breast cancer. In this paper, we introduce a novel method based on feature extraction and reduction for the detection of breast cancer in mammography images. First, we extract features from multiple pre-trained convolutional neural network (CNN) models, and then concatenate them. The most informative features are selected based on their mutual information with the target variable. Subsequently, the selected features can be classified using a machine learning algorithm. We evaluate our approach using four different machine learning algorithms: neural network (NN), k-nearest neighbor (kNN), random forest (RF), and support vector machine (SVM). Our results demonstrate that the NN-based classifier achieves an impressive accuracy of 92% on the RSNA dataset. This dataset is newly introduced and includes two views as well as additional features like age, which contributed to the improved performance. We compare our proposed algorithm with state-of-the-art methods and demonstrate its superiority, particularly in terms of accuracy and sensitivity. For the MIAS dataset, we achieve an accuracy as high as 94.5%, and for the DDSM dataset, an accuracy of 96% is attained. These results highlight the effectiveness of our method in accurately diagnosing breast lesions and surpassing existing approaches. Full article
(This article belongs to the Special Issue Advances in Object-Based Image Segmentation and Retrieval)
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16 pages, 3850 KiB  
Article
Hierarchical System for Recognition of Traffic Signs Based on Segmentation of Their Images
by Sergey Victorovich Belim, Svetlana Yuryevna Belim and Evgeniy Victorovich Khiryanov
Information 2023, 14(6), 335; https://doi.org/10.3390/info14060335 - 15 Jun 2023
Viewed by 1034
Abstract
This article proposes an algorithm for recognizing road signs based on a determination of their color and shape. It first searches for the edge segment of the road sign. The boundary curve of the road sign is defined by the boundary of the [...] Read more.
This article proposes an algorithm for recognizing road signs based on a determination of their color and shape. It first searches for the edge segment of the road sign. The boundary curve of the road sign is defined by the boundary of the edge segment. Approximating the boundaries of a road sign reveals its shape. The hierarchical road sign recognition system forms classes in the form of a sign. Six classes are at the first level. Two classes contain only one road sign. Signs are classified by the color of the edge segment at the second level of the hierarchy. The image inside the edge segment is cut at the third level of the hierarchy. The sign is then identified based on a comparison of the pattern. A computer experiment was carried out on two collections of road signs. The proposed algorithm has a high operating speed and a low percentage of errors. Full article
(This article belongs to the Special Issue Advances in Object-Based Image Segmentation and Retrieval)
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15 pages, 5629 KiB  
Article
Deep Feature Pyramid Hashing for Efficient Image Retrieval
by Adil Redaoui and Kamel Belloulata
Information 2023, 14(1), 6; https://doi.org/10.3390/info14010006 - 22 Dec 2022
Cited by 2 | Viewed by 1867
Abstract
Thanks to the success of deep learning, deep hashing has recently evolved as a leading method for large-scale image retrieval. Most existing hashing methods use the last layer to extract semantic information from the input image. However, these methods have deficiencies because semantic [...] Read more.
Thanks to the success of deep learning, deep hashing has recently evolved as a leading method for large-scale image retrieval. Most existing hashing methods use the last layer to extract semantic information from the input image. However, these methods have deficiencies because semantic features extracted from the last layer lack local information, which might impact the global system’s performance. To this end, a Deep Feature Pyramid Hashing DFPH is proposed in this study, which can fully utilize images’ multi-level visual and semantic information. Our architecture applies a new feature pyramid network designed for deep hashing to the VGG-19 model, so the model becomes able to learn the hash codes from various feature scales and then fuse them to create final binary hash codes. The experimental results performed on two widely used image retrieval datasets demonstrate the superiority of our method. Full article
(This article belongs to the Special Issue Advances in Object-Based Image Segmentation and Retrieval)
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