A review on visual content-based and users’ tags-based image annotation: methods and techniques

Bouchakwa, Mariam; Ayadi, Yassine; Amous, Ikram

doi:10.1007/s11042-020-08862-1

Cited by 14 publications

(9 citation statements)

References 223 publications

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“…Feature extraction is a technique for indexing and extracting visual content from images. Color, texture, shape and domain-specific features are examples of primitive or low-level image features [82]. Depending on the approach utilized, various annotation types are used to annotate images.…”

Section: Deep Learning For Image Annotationmentioning

confidence: 99%

Advanced Technology in Agriculture Industry by Implementing Image Annotation Technique and Deep Learning Approach: A Review

et al. 2022

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The implementation of intelligent technology in agriculture is seriously investigated as a way to increase agriculture production while reducing the amount of human labor. In agriculture, recent technology has seen image annotation utilizing deep learning techniques. Due to the rapid development of image data, image annotation has gained a lot of attention. The use of deep learning in image annotation can extract features from images and has been shown to analyze enormous amounts of data successfully. Deep learning is a type of machine learning method inspired by the structure of the human brain and based on artificial neural network concepts. Through training phases that can label a massive amount of data and connect them up with their corresponding characteristics, deep learning can conclude unlabeled data in image processing. For complicated and ambiguous situations, deep learning technology provides accurate predictions. This technology strives to improve productivity, quality and economy and minimize deficiency rates in the agriculture industry. As a result, this article discusses the application of image annotation in the agriculture industry utilizing several deep learning approaches. Various types of annotations that were used to train the images are presented. Recent publications have been reviewed on the basis of their application of deep learning with current advancement technology. Plant recognition, disease detection, counting, classification and yield estimation are among the many advancements of deep learning architecture employed in many applications in agriculture that are thoroughly investigated. Furthermore, this review helps to assist researchers to gain a deeper understanding and future application of deep learning in agriculture. According to all of the articles, the deep learning technique has successfully created significant accuracy and prediction in the model utilized. Finally, the existing challenges and future promises of deep learning in agriculture are discussed.

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Section: Deep Learning For Image Annotationmentioning

confidence: 99%

Advanced Technology in Agriculture Industry by Implementing Image Annotation Technique and Deep Learning Approach: A Review

et al. 2022

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“…We investigate the effects of a visual narrative intervention on reducing interview performance anxiety by incorporating images into serious storytelling for images' ubiquitous use in everyday life and ability for annotation and classification [8,29]. We hypothesise that a minimal intervention, in which interviewees prepare images to assist serious storytelling will alleviate interview performance anxiety.…”

Section: The Present Studymentioning

confidence: 99%

Seeing is relieving: effects of serious storytelling with images on interview performance anxiety

Lin-Stephens

Manuguerra

Bulbert

2022

Multimed Tools Appl

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Serious storytelling as a media genre has the potential to accentuate the benefits of narrative interventions in health and education. To inform its application, it is necessary to identify effects of sensory inputs. Here, we focus on visual stimuli and observe their effects on an anxiety condition. We examine whether serious storytelling incorporating images, a type of basic visual stimuli, may reduce interview performance anxiety. In a double-blind randomised control trial, 69 participants with matched levels of anxiety received serious storytelling interview training and were allocated to exposure (image-based preparation) and control (standard preparation) groups. A week later, participants attended individual interviews with two independent interviewers and reported their interview anxiety. Analyses revealed a positive relationship between generalised anxiety and some dimensions of interview anxiety, but serious storytelling with images predicted a reduction in interview performance anxiety (effect size at the median value of covariates on a visual analogue scale with the range 0–100: -36.7, 95% CI [−54.7, −2.5]). Low participation burden in the brief intervention was confirmed through a deductive thematic analysis. The images were analysed based on format type and origin to inform further inquiries. This study yielded empirical findings with implications of media and technology development for serious storytelling. Seeing images of experiences during interview preparation was associated with a relief of interviewees’ anxiety towards interview performance, but further studies are necessary to consolidate the evidence for visual narrative applications in health and education.

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“…e pheromone distribution between the sample and the clustering center is w kh , and the pheromone distribution is between the sample and the clustering center. In the search process of the algorithm, the probability of sample points being assigned to each cluster center is calculated by the formula shown in equation (5), where α and β denote the relative importance of pheromone and heuristic factor, respectively, M is the total number of ants (h ∈ [−1, M]), q ∈ [−1, 1] is a given parameter, randomly generated R ∈ [−1, 1], and t is the number of iterations. A m (i) is the set of samples outside the taboo table, and k is the kth element of the taboo table, the kth sample traveled by ant m.…”

Section: Construction Of Visual Saliency Model Based On K-meansmentioning

confidence: 99%

“…And financial resources consumed in the information transfer between remote sensing data acquisition and distribution to the thematic applications for end-users, which urgently need to propose more efficient methods and technical systems systematically to give engineering solutions. It is a hot research topic in the field of high-resolution remote sensing image analysis and application, but there are still obvious limitations when existing methods or algorithms are applied to the object recognition of high-resolution remote sensing ground cover [5,6]. e recognition tasks for different elements have different emphasis on feature selection and require sufficient industry expertise and rich a priori knowledge, which leads to the lack of universal application of the method.…”

Section: Introductionmentioning

confidence: 99%

Construction of a Visual Saliency Model for Neighborhood Building Landmarks Based on K‐Means Clustering

Qiao

2021

Advances in Civil Engineering

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In this paper, firstly, based on the quantitative relationship between K-means clustering and visual saliency of neighborhood building landmarks, the weights occupied by each index of composite visual factors are obtained by using multiple statistical regression methods, and, finally, we try to construct a saliency model of multiple visual index composites and analyze and test the model. As regards decomposition and quantification of visual saliency influencing factors, to describe and quantify these visual significance factors of the landmarks, the significant factors are decomposed into several quantifiable secondary indicators. Considering that the visual saliency of the landmarks in the neighborhood is reflected by the variance of the influencing factors and that the scope of the landmarks is localized, the local outlier detection algorithm is used to solve the variance of the secondary indicators. Since the visual significance of neighborhood building landmarks is influenced by a combination of influencing factors, the overall difference degree of secondary indicators is calculated by K-means clustering. To facilitate the factor calculation, a factor-controlled virtual environment was built to carry out the experimental study of landmark perception and calculate the different degrees of each index of the building. The data of visual indicators of the neighborhood buildings for this experiment were also collected, and the significance values of the neighborhood buildings were calculated. The influence weights of the indicators were obtained by using multiple linear regression analysis, the visual significance model of the landmarks of the neighborhood buildings in the factor-controlled environment was constructed, and the model was analyzed and tested.

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A review on visual content-based and users’ tags-based image annotation: methods and techniques

Cited by 14 publications

References 223 publications

Advanced Technology in Agriculture Industry by Implementing Image Annotation Technique and Deep Learning Approach: A Review

Advanced Technology in Agriculture Industry by Implementing Image Annotation Technique and Deep Learning Approach: A Review

Seeing is relieving: effects of serious storytelling with images on interview performance anxiety

Construction of a Visual Saliency Model for Neighborhood Building Landmarks Based on K‐Means Clustering

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