Image Content Research Articles

FACSNet: Forensics aided content selection network for heterogeneous image steganalysis

The main goal of image steganalysis, as a technique of confrontation with steganography, is to determine the presence or absence of secret information in conjunction with the specific statistical characteristics of the carrier. With the development of deep learning technology in recent years, the performance of steganography has been gradually enhanced. Especially for the complex reality environment, the image content is mixed and heterogeneous, which brings great challenges to the practical application of image steganalysis technology. In order to solve this problem, we design a forensics aided content selection network (FACSNet) for heterogeneous image steganalysis. Considering the heterogeneous situation of real images, a forensics aided module is introduced to pre-categorise the images to be tested, so that the network is able to detect different categories of images in a more targeted way. The complexity of the images is also further analysed and classified using the content selection module to train a more adapted steganalyser. By doing this, the network is allowed to achieve better performance in recognising and classifying the heterogeneous images for detection. Experimental results show that our designed FACSNet is able to achieve excellent detection performance in heterogeneous environments, improving the detection accuracy by up to 7.14% points, with certain robustness and practicality.

Intelligent Sentinel satellite image processing technology for land cover mapping

Purpose. This article proposes to develop an intelligent Sentinel satellite image processing technology for land cover mapping using convolutional neural networks. The result will be an image with improved spatial resolution. Methodology. The paper presents a technology using a combination of biquadratic interpolation, histogram alignment, PCA transform, as well as a parallel residual architecture of convolutional neural networks. The technology increases the information content of Sentinel-2 optical images by combining 10 and 20-meter resolution data, resulting in primary 20-meter images with improved spatial resolution. Findings. The root mean square error (RMSE = 3.64) indicates a high accuracy in reproducing the spectral properties of the images. The correlation coefficient (CC = 0.997) confirms a high linear relationship between the estimated and observed images. The low value of Spectral Angle Mapper (SAM = 0.52) with the high Universal Image Quality Index (UIQI = 0.999) indicates high quality and structural similarity between the synthesized and reference images. These results confirm the proposed technology’s effectiveness in enhancing the spatial resolution of Sentinel satellite images. Originality. Traditional pansharpening methods of multispectral images developed for satellite images with panchromatic channels cannot be directly applied to Sentinel multispectral data, because these images do not contain a panchromatic channel. In addition, atmospheric conditions and the presence of clouds affect the quality of optical images, complicating their further thematic processing. The proposed technology, using biquadratic interpolation, histogram alignment, convolutional neural networks, and PCA transformation, removes clouds and enhances the spatial resolution of the primary 20-meter optical satellite image channels of Sentinel-2. This technology reduces color distortion and increases the detail of digital optical images, which allows for more accurate analysis of the state of the earth’s surface. Practical value. The results obtained can be used to improve the methods for processing Sentinel satellite images, which provide high spatial resolution and accurate preservation of spectral characteristics. It provides the foundation for the development of new geographic information systems for land cover monitoring.

QualityNet: A multi-stream fusion framework with spatial and channel attention for blind image quality assessment

This study introduces a novel Blind Image Quality Assessment (BIQA) approach leveraging a multi-stream spatial and channel attention model. Our method addresses challenges posed by diverse image content and distortions by integrating feature maps from two distinct backbones. Through spatial and channel attention mechanisms, our algorithm prioritizes regions of interest, enhancing its ability to capture crucial image details. Extensive evaluations on four benchmark datasets demonstrate superior performance compared to existing methods, closely aligning with human perceptual assessment. Our approach exhibits exceptional generalization capabilities on both authentic and synthetic distortion databases. Moreover, it demonstrates a distinctive focus on perceptual foreground information, enhancing its practical applicability. Thorough quantitative analyses underscore the algorithm’s superior performance, establishing its dominance over existing methods.

Research on Image Style Transfer Based on a Generative Model

The generation of artificial intelligence technology is rapidly developing. As a representative, Sora created in text, image rendering, and video production shows excellent ability. This article discusses the image-style transmission algorithm based on generating a confrontation network (GAN) and compares it with the Sora model. Studies have shown that GAN performs well in image style transfer, especially in maintaining the consistency of the image content and generating high-quality images. In the future, the expected artificial intelligence technology will further improve the quality and diversity of generating content by combining the diffusion model and GAN and expanding its application potential in the fields of art creation and virtual reality.

Appearance-Based Gaze Estimation as a Benchmark for Eye Image Data Generation Methods

Data augmentation is commonly utilized to increase the size and diversity of training sets for deep learning tasks. In this study, we propose a novel application of an existing image generation approach in the domain of realistic eye images that leverages data collected from 40 subjects. This hybrid method combines the benefits of precise control over the image content provided by 3D rendering, while introducing the previously lacking photorealism and diversity into synthetic images through neural style transfer. We prove its general efficacy as a data augmentation tool for appearance-based gaze estimation when generated data are mixed with a sparse train set of real images. It improved the results for 39 out of 40 subjects, with an 11.22% mean and a 19.75% maximum decrease in gaze estimation error, achieving similar metrics for train and held-out subjects. We release our data repository of eye images with gaze labels used in this work for public access.

Boundary Between Art and Pornography: Differences in Image Identification Between Artificial Intelligence and Human Intelligence

This study examined whether Microsoft Azure and human participants differed significantly in their judgment of controversial image content (e.g., adult and violent content). Significant differences were observed between the Azure- and participant-awarded scores on all four indexes—Adult, Racy, Gore, and Event Description. The artificial intelligence made stricter judgments than did the participants. The introduction of artificial intelligence may facilitate the efficient judgment of images by website administrators. However, the standards upheld by artificial intelligence appear to be unique and stricter than those upheld by people. In the short term, the boundary between art and pornography may remain difficult to identify. Assessing the difference in judgments between artificial and human intelligence should be a topic of continual concern.

Image Captioning Based on Semantic Scenes.

With the development of artificial intelligence and deep learning technologies, image captioning has become an important research direction at the intersection of computer vision and natural language processing. The purpose of image captioning is to generate corresponding natural language descriptions by understanding the content of images. This technology has broad application prospects in fields such as image retrieval, autonomous driving, and visual question answering. Currently, many researchers have proposed region-based image captioning methods. These methods generate captions by extracting features from different regions of an image. However, they often rely on local features of the image and overlook the understanding of the overall scene, leading to captions that lack coherence and accuracy when dealing with complex scenes. Additionally, image captioning methods are unable to extract complete semantic information from visual data, which may lead to captions with biases and deficiencies. Due to these reasons, existing methods struggle to generate comprehensive and accurate captions. To fill this gap, we propose the Semantic Scenes Encoder (SSE) for image captioning. It first extracts a scene graph from the image and integrates it into the encoding of the image information. Then, it extracts a semantic graph from the captions and preserves semantic information through a learnable attention mechanism, which we refer to as the dictionary. During the generation of captions, it combines the encoded information of the image and the learned semantic information to generate complete and accurate captions. To verify the effectiveness of the SSE, we tested the model on the MSCOCO dataset. The experimental results show that the SSE improves the overall quality of the captions. The improvement in scores across multiple evaluation metrics further demonstrates that the SSE possesses significant advantages when processing identical images.

A Curricular Review of Radiology Education in a Master of Physician Assistant Studies Program.

Physician assistants/associates (PAs) are expected to be competent in ordering and interpreting diagnostic imaging. However, there are no further details outlining the educational expectations of PAs as it relates to radiology upon graduation. This can result in significant variability in the radiology curricula that PA students are taught and, consequently, hinder PAs' ability to work within their full scope of practice. Therefore, the purpose of this study was to map the radiology curriculum in a Master of Physician Assistant Studies (MPAS) program to elucidate radiological educational training before graduation. Quantitative curricular mapping was used to assess the 2021 to 2022 MPAS program for radiological involvement. Relevant course and session objectives related to radiology education were identified. In addition, educational learning material was reviewed for diagnostic imaging content. Formal radiological training was observed in 8 of 27 courses in the preclinical curriculum, with 4.35% of the total session objectives directed to radiological education. This formal exposure comprises 18.9 hours (1.71%) of curricular time. Informal diagnostic imaging exposure increased radiology education to approximately 29.5 hours (2.67%) of curricular time. One course (Diagnostic Imaging) focuses exclusively on radiology teaching and accounts for approximately 50% of the total radiologic teaching. X-ray ordering and interpretation received the greatest emphasis throughout the curriculum, while ultrasound received the least attention. Further integration of formal radiological education into PA programs should be considered with specific attention directed toward point-of-care ultrasound exposure and ordering/interpretation skills.

Multi-stage reasoning on introspecting and revising bias for visual question answering

Visual Question Answering (VQA) is a task that involves predicting an answer to a question depending on the content of an image. However, recent VQA methods have relied more on language priors between the question and answer rather than the image content. To address this issue, many debiasing methods have been proposed to reduce language bias in model reasoning. However, the bias can be divided into two categories: good bias and bad bias. Good bias can benefit to the answer prediction, while the bad bias may associate the models with the unrelated information. Therefore, instead of excluding good and bad bias indiscriminately in existing debiasing methods, we proposed a bias discrimination module to distinguish them. Additionally, bad bias may reduce the model’s reliance on image content during answer reasoning and thus attend little on image features updating. To tackle this, we leverage Markov theory to construct a Markov field with image regions and question words as nodes. This helps with feature updating for both image regions and question words, thereby facilitating more accurate and comprehensive reasoning about both the image content and question. To verify the effectiveness of our network, we evaluate our network on VQA v2 and VQA cp v2 datasets and conduct extensive quantity and quality studies to verify the effectiveness of our proposed network. Experimental resu- lts show that our network achieves significant performance against the previous state-of-the-art methods.

Where and Why Travelers Visit? Classifying Coastal Tourism Activities Using Geotagged Image Content from Social Media Data

Accurate information regarding the size, activity, and distribution of coastal tourists is essential for the effective management and planning of coastal tourism. In this study, geotagged photos uploaded to social network services were classified to identify coastal tourism activities. These activities were linked with spatial-scale data on tourist numbers estimated from social media data. To classify the activities, which included recreation, appreciation, education, and other activities, an image-supervised classification model was trained using 12,229 images, and the test accuracy was found to be 0.7244. On the Flickr platform, 43% of the image data located in the coastal land of South Korea are other activities, 39% are appreciation activities, and 18% are recreation and education activities. Other activities are mainly located in urban areas with a high population density and are spatially concentrated, while appreciation activities are mainly located in the natural environment and tend to be spatially spread out. Data on tourist activity categorization through content classification, combined with traditional tourist volume estimates, can help us understand previously overlooked information and context about a space.

Generating Artistic Images Through Neural Style Transfer Using Machine Learning and Image Manipulation Techniques

Neural Style Transfer (NST) is a computational technique that merges the content of one image with the stylistic characteristics of another, creating visually appealing compositions. Leveraging deep learning and image processing, this method separates andcombines the content and style of input images using convolutional neural networks (CNNs). The content representation of an image is captured by the high-level features extracted from a pre-trained CNN, while its style is characterized by the statistical correlations of features at multiple network layers. It explores the fusion of machine learning and image processing techniques in NST. Initially introduced by Gatys et al., NST has gained popularity due to its ability to generate artistic renditions and novel visualizations. The process involves extracting content and style features from separate images and optimizing a generated image to minimize differences in content and style representations. In this work, explored into the underlying concepts and technical aspectsof NST. This proceedings discussed the utilization of pre-trained CNN models like VGG19 or ResNet for feature extraction and how optimization algorithms, such as gradient descent, iteratively refine the generated image to achieve a fusion of content and style. Additionally, This development has been examined the impact of hyper parameters and layer choices on the quality of stylized outputs.Mainly this technology focuses on reduction of total variation loss, which we incur during the style transfer.These proceedings has carried on work with VGG19, XCEPTION, EfficientnetB7. These findings show how Convolutional Neural Networks can learn deep image representations and show how they may be used for sophisticated image synthesis andmodification.

Role of inter- and extra-lesion tissue, transfer learning, and fine-tuning in the robust classification of breast lesions

Accurate and unbiased classification of breast lesions is pivotal for early diagnosis and treatment, and a deep learning approach can effectively represent and utilize the digital content of images for more precise medical image analysis. Breast ultrasound imaging is useful for detecting and distinguishing benign masses from malignant masses. Based on the different ways in which benign and malignant tumors affect neighboring tissues, i.e., the pattern of growth and border irregularities, the penetration degree of the adjacent tissue, and tissue-level changes, we investigated the relationship between breast cancer imaging features and the roles of inter- and extra-lesional tissues and their impact on refining the performance of deep learning classification. The novelty of the proposed approach lies in considering the features extracted from the tissue inside the tumor (by performing an erosion operation) and from the lesion and surrounding tissue (by performing a dilation operation) for classification. This study uses these new features and three pre-trained deep neuronal networks to address the challenge of breast lesion classification in ultrasound images. To improve the classification accuracy and interpretability of the model, the proposed model leverages transfer learning to accelerate the training process. Three modern pre-trained CNN architectures (MobileNetV2, VGG16, and EfficientNetB7) are used for transfer learning and fine-tuning for optimization. There are concerns related to the neuronal networks producing erroneous outputs in the presence of noisy images, variations in input data, or adversarial attacks; thus, the proposed system uses the BUS-BRA database (two classes/benign and malignant) for training and testing and the unseen BUSI database (two classes/benign and malignant) for testing. Extensive experiments have recorded accuracy and AUC as performance parameters. The results indicate that the proposed system outperforms the existing breast cancer detection algorithms reported in the literature. AUC values of 1.00 are calculated for VGG16 and EfficientNet-B7 in the dilation cases. The proposed approach will facilitate this challenging and time-consuming classification task.

A robust coverless image-synthesized video steganography based on asymmetric structure

Due to the ability of hiding secret information without modifying image content, coverless image stegonagraphy has gained higher level of security and become a research hot spot. However, in existing methods, the issue of image order disruption during network transmission is overlooked. In this paper, the image-synthesized video carrier is proposed for the first time. The selected images which represent secret information are synthesized to a video in order, thus the image order will not be disrupted during transmission and the effective capacity is greatly increased. Additionally, an asymmetric structure is designed to improve the robustness, in which only the receiver utilizes a robust image retrieval algorithm to restore secret information. Specifically, certain images are randomly selected from a public image database to create multiple coverless image datasets (MCIDs), with each image in a CID mapped to hash sequence. Images are indexed based on secret segments and synthesized into videos. After that, the synthesized videos are sent to the receiver. The receiver decodes the video into frames, identifies the corresponding CID of each frame, retrieves original image, and restores the secret information with the same mapping rule. Experimental results indicate that the proposed method outperforms existing methods in terms of capacity, robustness, and security.

Discrete diffusion models with Refined Language-Image Pre-trained representations for remote sensing image captioning

RS image captioning (RSIC) utilizes natural language to provide a description of image content, assisting in the comprehension of object properties and relationships. Nonetheless, RS images are characterized by variations in object scales, distributions, and quantities, which make it challenging to obtain global semantic information and object connections. To enhance the accuracy of captions produced from RS images, this paper proposes a novel method referred to as Discrete Diffusion Models with Refined Language-Image Pre-trained representations (DDM-RLIP), leveraging an advanced discrete diffusion model (DDM) for nosing and denoising text tokens. DDM-RLIP is based on an advanced DDM-based method designed for natural pictures. The primary approach for refining image representations involves fine-tuning a CLIP image encoder on RS images, followed by adapting the transformer with an additional attention module to focus on crucial image regions and relevant words. Furthermore, experiments were conducted on three datasets, Sydney-Captions, UCM-Captions, and NWPU-Captions, and the results demonstrated the superior performance of the proposed method compared to conventional autoregressive models. On the NWPU-Captions dataset, the CIDEr score improved from 116.4 to 197.7, further validating the efficacy and potential of DDM-RLIP. The implementation codes for our approach DDM-RLIP are available at https://github.com/Leng-bingo/DDM-RLIP.

MinoritySalMix and adaptive semantic weight compensation for long-tailed classification

In real-world datasets, the widespread presence of a long-tailed distribution often leads models to become overly biased towards majority class samples while ignoring minority class samples. We propose a strategy called MASW (MinoritySalMix and adaptive semantic weight) to improve this problem. First, we propose a data augmentation method called MinoritySalMix (minority-saliency-mixing), which uses significance detection techniques to select significant regions from minority class samples as cropping regions and paste them into the same regions of majority class samples to generate brand new samples, thereby amplifying images containing important regions of minority class samples. Second, in order to make the label value information of the newly generated samples more consistent with the image content of the newly generated samples, we propose an adaptive semantic compensation factor. This factor provides more label value compensation for minority samples based on the different cropping areas, thereby making the new label values closer to the content of the newly generated samples. Improve model performance by generating more accurate new label value information. Finally, considering that some current re-sampling strategies generally lack flexibility in handling class sampling weight allocation and frequently require manual adjustment. We designed an adaptive weight function and incorporated it into the re-sampling strategy to achieve better sampling. The experimental results on three long-tailed datasets show that our method can effectively improve the performance of the model and is superior to most advanced long-tailed methods. Furthermore, we extended MinoritySalMix’s strategy to three balanced datasets for experimentation, and the results indicated that our method surpassed several advanced data augmentation techniques.

The Influence of Brand Image, Product Innovation, and Marketing Content on Purchasing Decisions Honda Scoopy Motorbikes in Denpasar City

Changes in consumer preferences in the motorcycle industry demand companies to enchance their marketing strategies to meet the need for efficient transportation amidst intense market competition. Denpasar serves as a prominent commercial hub, facilitating the acquisition of information. This research carried out in Denpasar, a city divided into four districts, with the objective of evaluating the impact of brand image, product development, and marketing content on the purchasing decisions for Honda Scoopy scooters in the region. A quantitative, descriptive research approach was employed. The target population included motorcycle riders who did not currently own a Honda Scoopy. A purposive sampling method was utilized, resulting in a sample 100 respondents. Data were collected using a structured survey consisting of 18 questions designed to measure each variable. The responses from the 100 participants were analyzed using multiple regression analysis with SPSS Statistics 26. The results indicate that brand image substantial impacts on consumer decision-making. Marketing content also affects these decisions, while product innovation has a substantial and positive effect on purchasing choices. The coefficient of determination (R²) is 73.4%, suggesting that brand image, product innovation, and marketing content collectively explain 73.4% of the variance in purchasing decisions. Factors that are not included in the regression model have an impact on the remaining 26.6%. Therefore, can surmised that brand image, product development, and marketing content significantly impact purchasing decisions.

Prompt-guided and multimodal landscape scenicness assessments with vision-language models.

Recent advances in deep learning and Vision-Language Models (VLM) have enabled efficient transfer to downstream tasks even when limited labelled training data is available, as well as for text to be directly compared to image content. These properties of VLMs enable new opportunities for the annotation and analysis of images. We test the potential of VLMs for landscape scenicness prediction, i.e., the aesthetic quality of a landscape, using zero- and few-shot methods. We experiment with few-shot learning by fine-tuning a single linear layer on a pre-trained VLM representation. We find that a model fitted to just a few hundred samples performs favourably compared to a model trained on hundreds of thousands of examples in a fully supervised way. We also explore the zero-shot prediction potential of contrastive prompting using positive and negative landscape aesthetic concepts. Our results show that this method outperforms a linear probe with few-shot learning when using a small number of samples to tune the prompt configuration. We introduce Landscape Prompt Ensembling (LPE), which is an annotation method for acquiring landscape scenicness ratings through rated text descriptions without needing an image dataset during annotation. We demonstrate that LPE can provide landscape scenicness assessments that are concordant with a dataset of image ratings. The success of zero- and few-shot methods combined with their ability to use text-based annotations highlights the potential for VLMs to provide efficient landscape scenicness assessments with greater flexibility.

Good Practices in using Instructional Images to Enhance Young Children's Linguistic Skills from the Viewpoint of Language Education Experts

The increasing emphasis on improving early childhood language education necessitates a deeper exploration of effective pedagogical tools, particularly visual aids. This study aims to examine the perspectives of language education specialists regarding best practices for employing images to enhance language development in early childhood settings. A targeted sample of 33 experts in language teaching, all working in early childhood education institutions in northern Jordan, was selected for the study. Qualitative research methodology was adopted, utilizing semi-structured interviews and grounded theory for data analysis. The findings identified a series of best practices for the use of instructional images to foster language development in young learners. Key practices included avoiding overly explicit interpretations of images by teachers, cultivating teachers’ confidence in children’s ability to interpret and engage with images independently, providing immediate feedback after children view images, allowing ample opportunities for children to articulate their interpretations of image content, ensuring clear and visible image presentation, selecting images that resonate with children’s real-life experiences, and accommodating individual differences among children. Based on these results, the study put forth several recommendations aimed at enhancing language acquisition through the strategic use of visual aids. The research highlights the critical role of creating a learning environment that effectively integrates images as a tool for promoting language skills in early childhood, encouraging a more interactive and engaging approach to language education.

A novel application of laser speckle imaging technique for prediction of hypoxic stress of apples

BackgroundFruit storage methods such as dynamic controlled atmosphere (DCA) technology enable adjusting the level of oxygen in the storage room, according to the physiological state of the product to slow down the ripening process. However, the successful application of DCA requires precise and reliable sensors of the oxidative stress of the fruit. In this study, respiration rate and chlorophyll fluorescence (CF) signals were evaluated after introducing a novel predictors of apples' hypoxic stress based on laser speckle imaging technique (LSI).ResultsBoth chlorophyll fluorescence and LSI signals were equally good for stress detection in principle. However, in an application with automatic detection based on machine learning models, the LSI signal proved to be superior, due to its stability and measurement repeatability. Moreover, the shortcomings of the CF signal appear to be its inability to indicate oxygen stress in tissues with low chlorophyll content but this does not apply to LSI. A comparison of different LSI signal processing methods showed that method based on the dynamics of changes in image content was better indicators of stress than methods based on measurements of changes in pixel brightness (inertia moment or laser speckle contrast analysis). Data obtained using the near-infrared laser provided better prediction capabilities, compared to the laser with red light.ConclusionsThe study showed that the signal from the scattered laser light phenomenon is a good predictor for the oxidative stress of apples. Results showed that effective prediction using LSI was possible and did not require additional signals. The proposed method has great potential as an alternative indicator of fruit oxidative stress, which can be applied in modern storage systems with a dynamically controlled atmosphere.

Contrastive learning explains the emergence and function of visual category-selective regions.

Modular and distributed coding theories of category selectivity along the human ventral visual stream have long existed in tension. Here, we present a reconciling framework-contrastive coding-based on a series of analyses relating category selectivity within biological and artificial neural networks. We discover that, in models trained with contrastive self-supervised objectives over a rich natural image diet, category-selective tuning naturally emerges for faces, bodies, scenes, and words. Further, lesions of these model units lead to selective, dissociable recognition deficits, highlighting their distinct functional roles in information processing. Finally, these pre-identified units can predict neural responses in all corresponding face-, scene-, body-, and word-selective regions of human visual cortex, under a highly constrained sparse positive encoding procedure. The success of this single model indicates that brain-like functional specialization can emerge without category-specific learning pressures, as the system learns to untangle rich image content. Contrastive coding, therefore, provides a unifying account of object category emergence and representation in the human brain.