Classification Of Photographs Research Articles

Automatic etiological classification of stroke thrombus digital photographs using a deep learning model

Automatic etiological classification of stroke thrombus digital photographs using a deep learning model

Profile Photograph Classification Performance of Deep Learning Algorithms Trained Using Cephalometric Measurements: A Preliminary Study.

Extraoral profile photographs are crucial for orthodontic diagnosis, documentation, and treatment planning. The purpose of this study was to evaluate classifications made on extraoral patient photographs by deep learning algorithms trained using grouped patient pictures based on cephalometric measurements. Cephalometric radiographs and profile photographs of 990 patients from the archives of Kocaeli University Faculty of Dentistry Department of Orthodontics were used for the study. FH-NA, FH-NPog, FMA and N-A-Pog measurements on patient cephalometric radiographs were carried out utilizing Webceph. 3 groups for every parameter were formed according to cephalometric values. Deep learning algorithms were trained using extraoral photographs of the patients which were grouped according to respective cephalometric measurements. 14 deep learning models were trained and tested for accuracy of prediction in classifying patient images. Accuracy rates of up to 96.67% for FH-NA groups, 97.33% for FH-NPog groups, 97.67% for FMA groups and 97.00% for N-A-Pog groups were obtained. This is a pioneering study where an attempt was made to classify clinical photographs using artificial intelligence architectures that were trained according to actual cephalometric values, thus eliminating or reducing the need for cephalometric X-rays in future applications for orthodontic diagnosis.

Skin-CAD: Explainable deep learning classification of skin cancer from dermoscopic images by feature selection of dual high-level CNNs features and transfer learning

Skin cancer (SC) significantly impacts many individuals' health all over the globe. Hence, it is imperative to promptly identify and diagnose such conditions at their earliest stages using dermoscopic imaging. Computer-aided diagnosis (CAD) methods relying on deep learning techniques especially convolutional neural networks (CNN) can effectively address this issue with outstanding outcomes. Nevertheless, such black box methodologies lead to a deficiency in confidence as dermatologists are incapable of comprehending and verifying the predictions that were made by these models. This article presents an advanced an explainable artificial intelligence (XAI) based CAD system named “Skin-CAD” which is utilized for the classification of dermoscopic photographs of SC. The system accurately categorises the photographs into two categories: benign or malignant, and further classifies them into seven subclasses of SC. Skin-CAD employs four CNNs of different topologies and deep layers. It gathers features out of a pair of deep layers of every CNN, particularly the final pooling and fully connected layers, rather than merely depending on attributes from a single deep layer. Skin-CAD applies the principal component analysis (PCA) dimensionality reduction approach to minimise the dimensions of pooling layer features. This also reduces the complexity of the training procedure compared to using deep features from a CNN that has a substantial size. Furthermore, it combines the reduced pooling features with the fully connected features of each CNN. Additionally, Skin-CAD integrates the dual-layer features of the four CNNs instead of entirely depending on the features of a single CNN architecture. In the end, it utilizes a feature selection step to determine the most important deep attributes. This helps to decrease the general size of the feature set and streamline the classification process. Predictions are analysed in more depth using the local interpretable model-agnostic explanations (LIME) approach. This method is used to create visual interpretations that align with an already existing viewpoint and adhere to recommended standards for general clarifications. Two benchmark datasets are employed to validate the efficiency of Skin-CAD which are the Skin Cancer: Malignant vs. Benign and HAM10000 datasets. The maximum accuracy achieved using Skin-CAD is 97.2 % and 96.5 % for the Skin Cancer: Malignant vs. Benign and HAM10000 datasets respectively. The findings of Skin-CAD demonstrate its potential to assist professional dermatologists in detecting and classifying SC precisely and quickly.

Machine Learning Analysis of Gaze Data for Enhanced Precision in Diagnosing Oral Mucosal Diseases.

The diagnosis of oral mucosal diseases is a significant challenge due to their diverse differential characteristics. Risk assessment of lesions by visual examination is a complex process due to the lack of definitive guidelines. This study aimed to improve this process by creating a diagnostic algorithm using gaze data acquired during oral mucosal disease examinations. A total of 78 dentists were included in this study. Tobii Pro Nano® (Tobii Technology) was used to acquire gaze data during clinical photographic visual examinations. Advanced analysis tools such as support vector machines and heatmaps were used to visualize the gazing tendencies of a group of skilled oral surgeons, focusing on the number of gazes per region and the gazing time ratios. The preliminary findings showed the possibility of visualizing gazing tendencies and identifying areas of importance for diagnosis. The classification of intraoral photographs based on gross features revealed the existence of an optimal examination method for each category and diagnostically significant areas. This novel approach to analyzing gaze data has the potential to refine diagnostic techniques and increase both accuracy and efficiency.

Classification of Color Fundus Photographs Using Fusion Extracted Features and Customized CNN Models.

This study focuses on overcoming challenges in classifying eye diseases using color fundus photographs by leveraging deep learning techniques, aiming to enhance early detection and diagnosis accuracy. We utilized a dataset of 6392 color fundus photographs across eight disease categories, which was later augmented to 17,766 images. Five well-known convolutional neural networks (CNNs)-efficientnetb0, mobilenetv2, shufflenet, resnet50, and resnet101-and a custom-built CNN were integrated and trained on this dataset. Image sizes were standardized, and model performance was evaluated via accuracy, Kappa coefficient, and precision metrics. Shufflenet and efficientnetb0demonstrated strong performances, while our custom 17-layer CNN outperformed all with an accuracy of 0.930 and a Kappa coefficient of 0.920. Furthermore, we found that the fusion of image features with classical machine learning classifiers increased the performance, with Logistic Regression showcasing the best results. Our study highlights the potential of AI and deep learning models in accurately classifying eye diseases and demonstrates the efficacy of custom-built models and the fusion of deep learning and classical methods. Future work should focus on validating these methods across larger datasets and assessing their real-world applicability.

Automatic Classification of Slit-Lamp Photographs by Imaging Illumination.

The aim of this study was to facilitate deep learning systems in image annotations for diagnosing keratitis type by developing an automated algorithm to classify slit-lamp photographs (SLPs) based on illumination technique. SLPs were collected from patients with corneal ulcer at Kellogg Eye Center, Bascom Palmer Eye Institute, and Aravind Eye Care Systems. Illumination techniques were slit beam, diffuse white light, diffuse blue light with fluorescein, and sclerotic scatter (ScS). Images were manually labeled for illumination and randomly split into training, validation, and testing data sets (70%:15%:15%). Classification algorithms including MobileNetV2, ResNet50, LeNet, AlexNet, multilayer perceptron, and k-nearest neighborhood were trained to distinguish 4 type of illumination techniques. The algorithm performances on the test data set were evaluated with 95% confidence intervals (CIs) for accuracy, F1 score, and area under the receiver operator characteristics curve (AUC-ROC), overall and by class (one-vs-rest). A total of 12,132 images from 409 patients were analyzed, including 41.8% (n = 5069) slit-beam photographs, 21.2% (2571) diffuse white light, 19.5% (2364) diffuse blue light, and 17.5% (2128) ScS. MobileNetV2 achieved the highest overall F1 score of 97.95% (CI, 97.94%-97.97%), AUC-ROC of 99.83% (99.72%-99.9%), and accuracy of 98.98% (98.97%-98.98%). The F1 scores for slit beam, diffuse white light, diffuse blue light, and ScS were 97.82% (97.80%-97.84%), 96.62% (96.58%-96.66%), 99.88% (99.87%-99.89%), and 97.59% (97.55%-97.62%), respectively. Slit beam and ScS were the 2 most frequently misclassified illumination. MobileNetV2 accurately labeled illumination of SLPs using a large data set of corneal images. Effective, automatic classification of SLPs is key to integrating deep learning systems for clinical decision support into practice workflows.

Optimizing CNN hyperparameters with genetic algorithms for face mask usage classification

Convolutional Neural Networks (CNNs) have gained significant traction in the field of image categorization, particularly in the domains of health and safety. This study aims to categorize the utilization of face masks, which is a vital determinant of respiratory health. Convolutional neural networks (CNNs) possess a high level of complexity, making it crucial to execute hyperparameter adjustment in order to optimize the performance of the model. The conventional approach of trial-and-error hyperparameter configuration often yields suboptimal outcomes and is time-consuming. Genetic Algorithms (GA), an optimization technique grounded in the principles of natural selection, were employed to identify the optimal hyperparameters for Convolutional Neural Networks (CNNs). The objective was to enhance the performance of the model, namely in the classification of photographs into two categories: those with face masks and those without face masks. The convolutional neural network (CNN) model, which was enhanced by the utilization of hyperparameters adjusted by a genetic algorithm (GA), demonstrated a commendable accuracy rate of 94.82% following rigorous testing and validation procedures. The observed outcome exhibited a 2.04% improvement compared to models that employed a trial and error approach for hyperparameter tuning. Our research exhibits exceptional quality in the domain of investigations utilizing Convolutional Neural Networks (CNNs). Our research integrates the resilience of Genetic Algorithms (GA), in contrast to previous studies that employed Convolutional Neural Networks (CNN) or conventional machine learning models without adjusting hyperparameters. This unique approach enhances the accuracy and methodology of hyperparameter tuning in Convolutional Neural Networks (CNNs).

Pit-mound microrelief on a forested slope drives infiltration and preferential flow after heavy rainfall – experiments with soil resistance monitoring and dye tracing

With the changing climate, short heavy rainfalls (SHRs) are becoming more frequent and intensive. The distribution of rainwater and subsurface runoff formation in forest soils can be strongly affected by soil disturbances and surface microrelief. Rainwater redistribution and the formation of preferential flow in response to natural and simulated SHRs were investigated in soil profiles on slopes forested by Norway spruce with a pit-mound microrelief formed by historical tree uprooting using two independent methods; soil electrical resistance monitoring and the application of dye tracer. In the deepest soil layers, resistance decreased in responses to natural SHRs on average by 46% in pits and by 11% on ‘smooth slope’ controls, reflecting the deeper redistribution of infiltrated water in pits. Dye tracing in the simulated rainfall revealed the formation of shallow biomat flow at the control plot. In contrast, the shallow lateral flow was focused by the pit and redirected into funnel flow underneath the pit bottom. The sum of medium and high dye concentration classes obtained with the support vector machine classification of profile photographs indicated a higher water entry into topsoil and subsoil layers in pit (44% and 31% of area, respectively) as compared to the control profile (16% and 8% of area, respectively). Leaving pit-mound microrelief as the natural legacy in forest soils can mitigate some of the negative hydrological effects of intensive forest management and may improve the water yields on forested slopes by redirecting shallow subsurface runoff and facilitating deep distribution of infiltrated water. Such beneficial effects of pit-mounds on the hydrology of forested slopes should be considered in future hydrological modelling and land management.

Can Plot-Level Photographs Accurately Estimate Tundra Vegetation Cover in Northern Alaska?

Plot-level photography is an attractive time-saving alternative to field measurements for vegetation monitoring. However, widespread adoption of this technique relies on efficient workflows for post-processing images and the accuracy of the resulting products. Here, we estimated relative vegetation cover using both traditional field sampling methods (point frame) and semi-automated classification of photographs (plot-level photography) across thirty 1 m2 plots near Utqiaġvik, Alaska, from 2012 to 2021. Geographic object-based image analysis (GEOBIA) was applied to generate objects based on the three spectral bands (red, green, and blue) of the images. Five machine learning algorithms were then applied to classify the objects into vegetation groups, and random forest performed best (60.5% overall accuracy). Objects were reliably classified into the following classes: bryophytes, forbs, graminoids, litter, shadows, and standing dead. Deciduous shrubs and lichens were not reliably classified. Multinomial regression models were used to gauge if the cover estimates from plot-level photography could accurately predict the cover estimates from the point frame across space or time. Plot-level photography yielded useful estimates of vegetation cover for graminoids. However, the predictive performance varied both by vegetation class and whether it was being used to predict cover in new locations or change over time in previously sampled plots. These results suggest that plot-level photography may maximize the efficient use of time, funding, and available technology to monitor vegetation cover in the Arctic, but the accuracy of current semi-automated image analysis is not sufficient to detect small changes in cover.

Towards a methodological approach to identify the main components used in historic photographs

In this work, a methodology is proposed to identify the components used in historic photographs using non-invasive and non-destructive characterisation techniques. The methodology provides an approach for analysing photographs by following three steps represented in a flowchart: firstly, the type of binder is determined by identifying morphological and structural characteristics; secondly, the presence or absence of a baryta layer is established; and finally, the presence of organic components used as part of a protective layer is determined. The characteristics and properties revealed using this methodology identify the type of historic photograph through different analytical techniques that provide science-based evidence for any qualitative observations first made. These techniques are interchangeable as they identify specific characteristics in the photographs by using infrared spectroscopy, Raman spectroscopy, infrared spectroscopic ellipsometry, confocal microscopy and atomic force microscopy, among others. As such, the methodology proposed facilitates a secure evidence-based classification of photographs and provides valuable information for their conservation.

A New U-Net Based Convolutional Neural Network for Estimating Caribou Lichen Ground Cover from Field-Level RGB Images

High-quality ground-truth data are critical for developing reliable Earth Observation (EO) based geospatial products. Conventional methods of collecting these data are either subject to an unknown amount of human error and bias or require extended time in the field to complete (i.e., point-intercept assessments). Digital photograph classification (DPC) may address these drawbacks. In this study, we first assess the performance of a DPC method developed through licensed software to estimate ground cover percentage (%) of bright lichens, a critical caribou forage in fall and winter when other food resources are scarce. We then evaluate the feasibility of replicating this workflow in an open-source environment with a modified U-net model to improve processing time and scalability. Our results indicate that DPC is appropriate for generating ground-truth data in support of large-scale EO-based lichen mapping within the boreal forests of eastern Canada. Our final open-sourced classification model, Lichen Convolutional Neural Network (LiCNN), is comparably accurate yet more efficient than the licensed workflow. Therefore, the LiCNN approach successfully addresses the mentioned shortcomings of conventional ground-truth data collection methods efficiently and without the need for specialized software.

Using superpixel- or pixel-based segmentation for efficient green roof digital image classification and rapid estimation of plant species cover

Green roofs are “nature-based solutions” that provide numerous ecosystem services in the context of urban green infrastructures. Plant species diversity and the associated vegetation communities, in strong interactions with green roof substrate, play a central role in the green infrastructure functioning. In order to better understand the influence of vegetation in relation with the co-benefits provided by green roofs as well as to select suitable species for these usually harsh environments, it is essential to be able to achieve accurate and long-term monitoring of plant communities. In this short communication, two free plugins recently developed for the open-source image analysis software Fiji (a distribution of the freely available ImageJ platform, initially dedicated to biological image analysis) were investigated for their capacity to rapidly and efficiently perform supervised machine-learning for the classification of green roof vegetation photographs, with the aim of estimating individual plant species abundance. Two simple methods are thus described using the Trainable Weka Pixel Segmentation (Arganda-Carreras et al., 2017) or the Trainable Superpixel Segmentation (Salinas Colina et al., 2018), which allowed for rapid, efficient and reproducible classification and estimation of multispecies colonized green roof regardless the color or shape similarities among species or ground cover materials. Finally, recommendations are made for the use of the Trainable Superpixel Segmentation which is particularly convenient for quick and efficient green roof image analysis.

Agreement on classification of clinical photographs of pigmentary lesions: exercise after a training course with young dermatologists.

Smartphone apps may help promoting the early diagnosis of melanoma. The reliability of specialist judgment on lesions should be assessed. Hereby, we evaluated the agreement of 6 young dermatologists, after a specific training. Clinical judgment was evaluated during 2 online sessions, 1 month apart, on a series of 45 pigmentary lesions. Lesions were classified as highly suspicious, suspicious, non-suspicious or not assessable. Cohen's and Fleiss' kappa were used to calculate intra- and inter-rater agreement. The overall intra-rater agreement was 0.42 (95% confidence interval - CI: 0.33-0.50), varying between 0.12-0.59 on single raters. The inter-rater agreement during the first phase was 0.29 (95% CI: 0.24-0.34). When considering the agreement for each category of judgment, kappa varied from 0.19 for not assessable to 0.48 for highly suspicious lesions. Similar results were obtained in the second exercise. The study showed a less than satisfactory agreement among young dermatologists. Our data point to the need for improving the reliability of the clinical diagnoses of melanoma especially when assessing small lesions and when dealing with thin melanomas at a population level.

항공사진을 이용한 딥러닝 기반 FASCAM 투발 적합지역 분석

This study attempts to find FASCAM(Family of Scatterable Mines) areas automatically from aerial photographs. Currently, manual FASCAM area analysis is conducted in military, and it takes a long time and can generate inconsistent results. In order to select FASCAM areas automatically, an object classification of aerial photographs was first performed using deep learning techniques. DeepLabV3+ was selected as the deep learning model that showed good performance in the aerial photography classification. Areas with high similarity compared to sample images of FASCAM areas was selected as FASCAM areas. As the image similarity comparison method, the color histogram was used. Finally, the proposed FASCAM area analysis method showed 85.71% of agreement with military engineering officers. If the method of this study is used, it is expected to be helpful in analyzing the battlefield environment and establishing an obstacle plan.

Hyperparameter Tuning and Automatic Image Augmentation for Deep Learning-Based Angle Classification on Intraoral Photographs-A Retrospective Study.

We aimed to assess the effects of hyperparameter tuning and automatic image augmentation for deep learning-based classification of orthodontic photographs along the Angle classes. Our dataset consisted of 605 images of Angle class I, 1038 images of class II, and 408 images of class III. We trained ResNet architectures for classification of different combinations of learning rate and batch size. For the best combination, we compared the performance of models trained with and without automatic augmentation using 10-fold cross-validation. We used GradCAM to increase explainability, which can provide heat maps containing the salient areas relevant for the classification. The best combination of hyperparameters yielded a model with an accuracy of 0.63–0.64, F1-score 0.61–0.62, sensitivity 0.59–0.65, and specificity 0.80–0.81. For all metrics, it was apparent that there was an ideal corridor of batch size and learning rate combinations; smaller learning rates were associated with higher classification performance. Overall, the performance was highest for learning rates of around 1–3 × 10−6 and a batch size of eight, respectively. Additional automatic augmentation improved all metrics by 5–10% for all metrics. Misclassifications were most common between Angle classes I and II. GradCAM showed that the models employed features relevant for human classification, too. The choice of hyperparameters drastically affected the performance of deep learning models in orthodontics, and automatic image augmentation resulted in further improvements. Our models managed to classify the dental sagittal occlusion along Angle classes based on digital intraoral photos.

A deep learning approach for detection of shallow anterior chamber depth based on the hidden features of fundus photographs

Background and ObjectivesPatients with angle-closure glaucoma (ACG) are asymptomatic until they experience a painful attack. Shallow anterior chamber depth (ACD) is considered a significant risk factor for ACG. We propose a deep learning approach to detect shallow ACD using fundus photographs and to identify the hidden features of shallow ACD. MethodsThis retrospective study assigned healthy subjects to the training (n = 1188 eyes) and test (n = 594) datasets (prospective validation design). We used a deep learning approach to estimate ACD and build a classification model to identify eyes with a shallow ACD. The proposed method, including subtraction of the input and output images of CycleGAN and a thresholding algorithm, was adopted to visualize the characteristic features of fundus photographs with a shallow ACD. ResultsThe deep learning model integrating fundus photographs and clinical variables achieved areas under the receiver operating characteristic curve of 0.978 (95% confidence interval [CI], 0.963–0.988) for an ACD ≤ 2.60 mm and 0.895 (95% CI, 0.868–0.919) for an ACD ≤ 2.80 mm, and outperformed the regression model using only clinical variables. However, the difference between shallow and deep ACD classes on fundus photographs was difficult to be detected with the naked eye. We were unable to identify the features of shallow ACD using the Grad-CAM. The CycleGAN-based feature images showed that area around the macula and optic disk significantly contributed to the classification of fundus photographs with a shallow ACD. ConclusionsWe demonstrated the feasibility of a novel deep learning model to detect a shallow ACD as a screening tool for ACG using fundus photographs. The CycleGAN-based feature map showed the hidden characteristic features of shallow ACD that were previously undetectable by conventional techniques and ophthalmologists. This framework will facilitate the early detection of shallow ACD to prevent overlooking the risks associated with ACG.

Photographs in the Photo Archive’s Old Stock in Gazi Husrev-beg’s Library in Sarajevo

Gazi Husrev-beg’s Library in Sarajevo, beside its rich stock of manuscripts, archival and museum collections, also houses a valuable collection of photographs within the photo archive’s stock, which is divided into the old and the new stock. The focus of this paper is on photographs from the photo archive’s old stock, taken at the time of the arrival of the Austro-Hungarian government, when the first professional photograph stores started up in this area. This paper aims at trying to determine whether the photographs from the photo archive’s old stock were signed by Anton Schadler, Walter Tausch, Ignatz Lederer, Ivica Lisac, Nusret Halačević and other foreign and domestic photographers who made a significant contribution to the development of photography in Bosnia and Herzegovina, or they were taken later in modest photograph stores. Moreover, based on the preserved lists of library materials and other archival materials stored in Gazi Husrev-beg’s Library in Sarajevo, the paper deals with the issue of forming the photo arhive’s stock and the ways to determine the processing, classification and systematization of photographs.

Classification of Pressure Injury Stages and Skin Damage Photographs by RNs and Nursing Students in Spain: An Observational Comparison Study.

The purpose of this study was to evaluate and classify pressure injuries (PIs) and other skin lesions according to the judgment of hospital-based RNs and nursing students who recently trained in wound care using photographs and comparing findings to those of wound care experts. Cross-sectional observational survey. RNs working at Manacor Hospital, Mallorca, Spain, and third-year nursing students from the University of the Balearic Islands. Data were collected in June 2017. The survey instrument was accompanied by a set of 24 photographs in 7 categories of skin lesions, such as PIs or moisture-associated skin damage, which the respondents were asked to classify. Descriptive analysis of the classifications was carried out, and levels of agreement using the chi-square test were compared with evaluations/classifications of a panel of experts. In total, 314 surveys (199 RNs and 115 students) were included in the analyses. Findings showed a wide variability among participants with levels of agreement, with expert opinion ranging from 91.4% to 15.61%. The levels of agreement were substantially lower for unstageable PIs (47.92%), those caused by moisture (53.50%), and mixed etiology (38.37%) than for stages 1-4 PIs. Of note, 62.12% of RNs had not received PI training or continuing education during the previous 2 years. This lack of education was statistically significantly associated with the accuracy of the classification made by the RNs for some of the photographs evaluated such as moisture-associated skin damage. We found discrepancies in the classification of PIs and other wounds/lesions among experts, RNs, and students. The regular provision of up-to-date information in training and continuing education programs is imperative to maintain nurses' abilities to identify and classify PIs and other skin-related damage.

Automated deep learning in ophthalmology: AI that can build AI.

The purpose of this review is to describe the current status of automated deep learning in healthcare and to explore and detail the development of these models using commercially available platforms. We highlight key studies demonstrating the effectiveness of this technique and discuss current challenges and future directions of automated deep learning. There are several commercially available automated deep learning platforms. Although specific features differ between platforms, they utilise the common approach of supervised learning. Ophthalmology is an exemplar speciality in the area, with a number of recent proof-of-concept studies exploring classification of retinal fundus photographs, optical coherence tomography images and indocyanine green angiography images. Automated deep learning has also demonstrated impressive results in other specialities such as dermatology, radiology and histopathology. Automated deep learning allows users without coding expertise to develop deep learning algorithms. It is rapidly establishing itself as a valuable tool for those with limited technical experience. Despite residual challenges, it offers considerable potential in the future of patient management, clinical research and medical education. http://links.lww.com/COOP/A44.

Recognition and Classification of Homogeneous Landscape With Visitor–Employed Photography and Cloud Image Annotation API—An Example of the Riverscape in Nihonbashi, Tokyo, Japan

Effective classification of landscape photographs is a vital step in data processing and environment analysis. With the popularity of crowdsourcing geo-information, an increasing number of studies have used geotagged photographs to visualize how people perceive and interact with destinations and explore the aesthetic, cultural, and recreational value of the areas. In recent years, machine-learning algorithms for image recognition have dramatically improved the efficiency of the assignment of keywords and provide possibilities for the automatic classification of numerous photographs. However, the applicability of such methods for the practical landscape classification is still not clear, especially for the photographs presenting a homogeneous landscape that has similar characteristics. This study developed a semi-automatic classifier for homogeneous landscape photographs by using Google Cloud Vision API and multi-level hierarchical clustering. The classifier was applied to the classification of urban riverscape photographs, which is a typical example of homogeneous landscapes in Nihonbashi, Tokyo, Japan. The riverscapes can be classified into 9 characteristic groups by the classifier and the visual impression of these groups matches well with our intuitive feeling. A confusion matrix showed that the overall accuracy was 82.61%, indicating a strong agreement between the classifier and manual classification. Therefore, the classifier is practical for classifying homogeneous riverscape photographs. Such methodology also provides the possibility of public participation in the assessing process, which, in turn, contributes to urban tourism management.