1
|
Sousa AD, Silva PHDS, Silva RRV, Rodrigues FAÀ, Medeiros FNS. CBIR-SAR System Using Stochastic Distance. SENSORS (BASEL, SWITZERLAND) 2023; 23:6080. [PMID: 37447929 DOI: 10.3390/s23136080] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Revised: 06/27/2023] [Accepted: 06/29/2023] [Indexed: 07/15/2023]
Abstract
This article proposes a system for Content-Based Image Retrieval (CBIR) using stochastic distance for Synthetic-Aperture Radar (SAR) images. The methodology consists of three essential steps for image retrieval. First, it estimates the roughness (α^) and scale (γ^) parameters of the GI0 distribution that models SAR data in intensity. The parameters of the model were estimated using the Maximum Likelihood Estimation and the fast approach of the Log-Cumulants method. Second, using the triangular distance, CBIR-SAR evaluates the similarity between a query image and images in the database. The stochastic distance can identify the most similar regions according to the image features, which are the estimated parameters of the data model. Third, the performance of our proposal was evaluated by applying the Mean Average Precision (MAP) measure and considering clippings from three radar sensors, i.e., UAVSAR, OrbiSaR-2, and ALOS PALSAR. The CBIR-SAR results for synthetic images achieved the highest MAP value, retrieving extremely heterogeneous regions. Regarding the real SAR images, CBIR-SAR achieved MAP values above 0.833 for all polarization channels for image samples of forest (UAVSAR) and urban areas (ORBISAR). Our results confirmed that the proposed method is sensitive to the degree of texture, and hence, it relies on good estimates. They are inputs to the stochastic distance for effective image retrieval.
Collapse
Affiliation(s)
- Alcilene Dalília Sousa
- Informatics Systems, Federal University of Piaui, Picos 64607-825, Piaui, Brazil
- Teleinformatics Engineering, Federal University of Ceara, Fortaleza 60455-970, Ceara, Brazil
| | | | | | | | | |
Collapse
|
2
|
Wang B, Gao Y, Yuan X, Xiong S. Local R-Symmetry Co-Occurrence: Characterising Leaf Image Patterns for Identifying Cultivars. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022; 19:1018-1031. [PMID: 33055018 DOI: 10.1109/tcbb.2020.3031280] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Leaf image recognition techniques have been actively researched for plant species identification. However it remains unclear whether analysing leaf patterns can provide sufficient information for further differentiating cultivars. This paper reports our attempt on cultivar recognition from leaves as a general very fine-grained pattern recognition problem, which is not only a challenging research problem but also important for cultivar evaluation, selection and production in agriculture. We propose a novel local R-symmetry co-occurrence method for characterising discriminative local symmetry patterns to distinguish subtle differences among cultivars. Through scalable and moving R-relation radius pairs, we generate a set of radius symmetry co-occurrence matrices (RsCoM)and their measures for describing the local symmetry properties of interior regions. By varying the size of the radius pair, the RsCoM measures local R-symmetry co-occurrence from global/coarse to fine scales. A new two-phase strategy of analysing the distribution of local RsCoM measures is designed to match the multiple scale appearance symmetry pattern distributions of similar cultivar leaf images. We constructed three leaf image databases, SoyCultivar, CottCultivar, and PeanCultivar, for an extensive experimental evaluation on recognition across soybean, cotton and peanut cultivars. Encouraging experimental results of the proposed method in comparison with the state-of-the-art leaf species recognition methods demonstrate the effectiveness of the proposed method for cultivar identification, which may advance the research in leaf recognition from species to cultivar.
Collapse
|
3
|
Plant Leaves Recognition Based on a Hierarchical One-Class Learning Scheme with Convolutional Auto-Encoder and Siamese Neural Network. Symmetry (Basel) 2021. [DOI: 10.3390/sym13091705] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
In this paper, we propose a novel method for plant leaves recognition by incorporating an unsupervised convolutional auto-encoder (CAE) and Siamese neural network in a unified framework by considering Siamese as an alternative to the conventional loss of CAE. Rather than the conventional exploitation of CAE and Siamese, in our case we have proposed to extend CAE for a novel supervised scenario by considering it as one-class learning classifier. For each class, CAE is trained to reconstruct its positive and negative examples and Siamese is trained to distinguish the similarity and the dissimilarity of the obtained examples. On the contrary and asymmetric to the related hierarchical classification schemes which require pre-knowledge on the dataset being recognized, we propose a hierarchical classification scheme that doesn’t require such a pre-knowledge and can be employed by non-experts automatically. We cluster the dataset to assemble similar classes together. A test image is first assigned to the nearest cluster, then matched to one class from the classes that fall under the determined cluster using our novel one-class learning classifier. The proposed method has been evaluated on the ImageCLEF2012 dataset. Experimental results have proved the superiority of our method compared to several state-of-the art methods.
Collapse
|
4
|
Progressive Transfer Learning Approach for Identifying the Leaf Type by Optimizing Network Parameters. Neural Process Lett 2021. [DOI: 10.1007/s11063-021-10521-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
|
5
|
Zhang D. Intelligent recognition of dance training movements based on machine learning and embedded system. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2021. [DOI: 10.3233/jifs-189954] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Recognizing human movement is an important research topic in the field of human-computer interaction, and people expect it to be used in smart homes, virtual reality, and electronic games. Based on the interaction between humans and computers, more and more attention has been paid, especially in the field of smart home action recognition. Through observation, people can understand the intention of intelligent interaction is included in the main part. However, the current recognition algorithms still cannot meet the actual requirements of the accuracy, real-time and robustness of human motion recognition. Especially in order to recognize complex human movements in real time, it is imperative to solve several problems in motion capture and recognition. Establishing the feature parameter angle of the feature vector space of motion data, using the pre-recognition algorithm is based on multi-class support vector machines. The motion recognition algorithm takes advantage of the accurate and fast classification function of svm. Based on the structural differences of the motion data, most of the data can be correctly identified. The optimal motion recognition algorithm uses hmm to correct the svm error recognition result through the random constraint relationship between the error recognition data and the actual label. Based on data simulation and analysis, each variable determined by the grid search algorithm has the highest accuracy in the optimization of each variable of the support vector machine. Finally, a smart home simulation experiment interactive system was built, and a local database was created, including 1,300 processes. The real-time algorithm uses the data in the local database for training and testing. Experimental results show that the motion recognition algorithm in this paper improves the accuracy and robustness of complex motion recognition. While meeting the real-time recognition conditions, the correct answer rate of the final operation can reach 9.6%. The human motion trajectory recognition system uses the three-dimensional trajectory of gestures to recognize motion. The information in the three-dimensional space is more comprehensive, and the orbit recognition is more robust.
Collapse
Affiliation(s)
- Dixin Zhang
- College of Music and Dance, Zhengzhou University of Technology, Zhengzhou, China
| |
Collapse
|
6
|
|
7
|
Zhang S, Huang W, Huang YA, Zhang C. Plant species recognition methods using leaf image: Overview. Neurocomputing 2020. [DOI: 10.1016/j.neucom.2019.09.113] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
|
8
|
Abstract
Plants are ubiquitous in human life. Recognizing an unknown plant by its leaf image quickly is a very interesting and challenging research. With the development of image processing and pattern recognition, plant recognition based on image processing has become possible. Bag of features (BOF) is one of the most powerful models for classification, which has been used for many projects and studies. Dual-output pulse-coupled neural network (DPCNN) has shown a good ability for texture features in image processing such as image segmentation. In this paper, a method based on BOF and DPCNN (BOF_DP) is proposed for leaf classification. BOF_DP achieved satisfactory results in many leaf image datasets. As it is hard to get a satisfactory effect on the large dataset by a single feature, a method (BOF_SC) improved from bag of contour fragments is used for shape feature extraction. BOF_DP and LDA (linear discriminant analysis) algorithms are, respectively, employed for textual feature extraction and reducing the feature dimensionality. Finally, both features are used for classification by a linear support vector machine (SVM), and the proposed method obtained higher accuracy on several typical leaf datasets than existing methods.
Collapse
|
9
|
Zamzami N, Bouguila N. High-dimensional count data clustering based on an exponential approximation to the multinomial Beta-Liouville distribution. Inf Sci (N Y) 2020. [DOI: 10.1016/j.ins.2020.03.028] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
|
10
|
Chaudhury A, Barron JL. Plant Species Identification from Occluded Leaf Images. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2020; 17:1042-1055. [PMID: 30295626 DOI: 10.1109/tcbb.2018.2873611] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
We present an approach to identify the plant species from the contour information from occluded leaf image using a database of full plant leaves. Although contour based 2D shape matching has been studied extensively in the last couple of decades, matching occluded leaves with full leaf databases is an open and little worked on problem. Classifying occluded plant leaves is even more challenging than full leaf matching because of large variations and complexity of leaf structures. Matching an occluded contour with all the full contours in a database is an NP-hard problem, so our algorithm is necessarily suboptimal. First, we represent the 2D contour points as a β-Spline curve. Then, we extract interest points on these curves via the Discrete Contour Evolution (DCE) algorithm. We use subgraph matching using the DCE points as graph nodes, which produces a number of open curves for each closed leaf contour. Next, we compute the similarity transformation parameters (translation, rotation, and uniform scaling) for each open curve. We then "overlay" each open curve with the inverse similarity transformed occluded curve and use the Fréchet distance metric to measure the quality of the match, retaining the best η matched curves. Since the Fréchet metric is cheap to compute but not perfectly correlated with the quality of the match, we formulate an energy functional that is well correlated with the quality of the match, but is considerably more expensive to compute. The functional uses local and global curvature, Shape Context descriptors, and String Cut features. We minimize this energy functional using a convex-concave relaxation framework. The curve among these best η curves, that has the minimum energy, is considered to be the best overall match with the occluded leaf. Experiments on three publicly available leaf image database shows that our method is both effective and efficient, outperforming other current state-of-the-art methods. Occlusion is measured as the percentage of the overall contour (and not leaf area) that is missing. We show that our algorithm can, even for leaves with a high amounts of occlusion (say 50 percent occlusion), still identify the best full leaf match from the databases.
Collapse
|
11
|
Development of automated hybrid intelligent system for herbs plant classification and early herbs plant disease detection. Neural Comput Appl 2019. [DOI: 10.1007/s00521-019-04634-7] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
|
12
|
Wang B, Gao Y, Sun C, Blumenstein M, La Salle J. Chord Bunch Walks for Recognizing Naturally Self-Overlapped and Compound Leaves. IEEE TRANSACTIONS ON IMAGE PROCESSING : A PUBLICATION OF THE IEEE SIGNAL PROCESSING SOCIETY 2019; 28:5963-5976. [PMID: 31199259 DOI: 10.1109/tip.2019.2921526] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Effectively describing and recognizing leaf shapes under arbitrary variations, particularly from a large database, remains an unsolved problem. In this research, we attempted a new strategy of describing leaf shapes by walking and measuring along a bunch of chords that pass through the shape. A novel chord bunch walks (CBW) descriptor is developed through the chord walking behavior that effectively integrates the shape image function over the walked chord to reflect both the contour features and the inner properties of the shape. For each contour point, the chord bunch groups multiple pairs of chords to build a hierarchical framework for a coarse-to-fine description that can effectively characterize not only the subtle differences among leaf margin patterns but also the interior part of the shape contour formed inside a self-overlapped or compound leaf. Instead of using optimal correspondence based matching, a Log-Min distance that encourages one-to-one correspondences is proposed for efficient and effective CBW matching. The proposed CBW shape analysis method is invariant to rotation, scaling, translation, and mirror transforms. Five experiments, including image retrieval of compound leaves, image retrieval of naturally self-overlapped leaves, and retrieval of mixed leaves on three large scale datasets, are conducted. The proposed method achieved large accuracy increases with low computational costs over the state-of-the-art benchmarks, which indicates the research potential along this direction.
Collapse
|
13
|
Carneiro AC, Lopes JG, Souza MM, Rocha Neto JF, Araújo FH, Silva RR, Medeiros FN, Bezerra FN. Parameter optimization of a multiscale descriptor for shape analysis on healthcare image datasets. Pattern Recognit Lett 2019. [DOI: 10.1016/j.patrec.2019.06.017] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
|
14
|
Araújo FH, Silva RR, Ushizima DM, Rezende MT, Carneiro CM, Campos Bianchi AG, Medeiros FN. Deep learning for cell image segmentation and ranking. Comput Med Imaging Graph 2019; 72:13-21. [DOI: 10.1016/j.compmedimag.2019.01.003] [Citation(s) in RCA: 47] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2018] [Revised: 12/03/2018] [Accepted: 01/15/2019] [Indexed: 12/27/2022]
|
15
|
Chaudhury A, Barron JL. Occluded Leaf Matching with Full Leaf Databases Using Explicit Occlusion Modelling. 2018 15TH CONFERENCE ON COMPUTER AND ROBOT VISION (CRV) 2018. [DOI: 10.1109/crv.2018.00012] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/19/2023]
|
16
|
Wäldchen J, Mäder P. Plant Species Identification Using Computer Vision Techniques: A Systematic Literature Review. ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING : STATE OF THE ART REVIEWS 2018; 25:507-543. [PMID: 29962832 PMCID: PMC6003396 DOI: 10.1007/s11831-016-9206-z] [Citation(s) in RCA: 70] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/01/2016] [Accepted: 11/24/2016] [Indexed: 05/04/2023]
Abstract
Species knowledge is essential for protecting biodiversity. The identification of plants by conventional keys is complex, time consuming, and due to the use of specific botanical terms frustrating for non-experts. This creates a hard to overcome hurdle for novices interested in acquiring species knowledge. Today, there is an increasing interest in automating the process of species identification. The availability and ubiquity of relevant technologies, such as, digital cameras and mobile devices, the remote access to databases, new techniques in image processing and pattern recognition let the idea of automated species identification become reality. This paper is the first systematic literature review with the aim of a thorough analysis and comparison of primary studies on computer vision approaches for plant species identification. We identified 120 peer-reviewed studies, selected through a multi-stage process, published in the last 10 years (2005-2015). After a careful analysis of these studies, we describe the applied methods categorized according to the studied plant organ, and the studied features, i.e., shape, texture, color, margin, and vein structure. Furthermore, we compare methods based on classification accuracy achieved on publicly available datasets. Our results are relevant to researches in ecology as well as computer vision for their ongoing research. The systematic and concise overview will also be helpful for beginners in those research fields, as they can use the comparable analyses of applied methods as a guide in this complex activity.
Collapse
Affiliation(s)
- Jana Wäldchen
- Department Biogeochemical Integration, Max Planck Institute for Biogeochemistry, Hans Knöll Strasse 10, 07745 Jena, Germany
| | - Patrick Mäder
- Software Engineering for Safety-Critical Systems, Technische Universität Ilmenau, Helmholtzplatz 5, 98693 Ilmenau, Germany
| |
Collapse
|
17
|
|
18
|
Tomaszewski D, Górzkowska A. Is Shape of a Fresh and Dried Leaf the Same? PLoS One 2016; 11:e0153071. [PMID: 27045956 PMCID: PMC4821626 DOI: 10.1371/journal.pone.0153071] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2015] [Accepted: 03/23/2016] [Indexed: 11/18/2022] Open
Abstract
Plants kept as dried herbarium specimens share many characteristics with their living counterparts, but there are some substantial differences between them. Due to dehydration, leaves of herbarium specimens change not only their mass and colour, but in many cases change their dimensions, too. The present study aimed to determine whether leaf shape changes during the drying process. A total of 794 pairs of fresh and dried leaves or leaflets of 22 plant taxa were studied. The shape of the blades was quantified using elliptic Fourier analysis combined with principal component analysis. In addition, area and mass of the leaves were measured. Statistical tests were applied for comparing fresh and dried leaves. The results indicate that the preservation process of pressing and drying plants for herbarium purposes causes changes in leaf shape. In general, the shape changes were directional. As the shape of fresh and dried plants is different, it is strongly recommended that shape analyses should be performed on datasets containing either of the leaf types.
Collapse
Affiliation(s)
- Dominik Tomaszewski
- Institute of Dendrology of the Polish Academy of Sciences, Parkowa 5, PL-62-035, Kórnik, Poland
- * E-mail:
| | - Angelika Górzkowska
- Poznań University of Life Sciences, Faculty of Forestry, Wojska Polskiego 71c, PL-60-625, Poznań, Poland
| |
Collapse
|
19
|
|