Visual Features Research Articles

A Comprehensive Review of Image Line Segment Detection and Description: Taxonomies, Comparisons, and Challenges.

An image line segment is a fundamental low-level visual feature that delineates straight, slender, and uninterrupted portionsof objects and scenarios within images. Detection and description of line segments lay the basis for numerous vision tasks. Althoughmany studies have aimed to detect and describe line segments, a comprehensive review is lacking, obstructing their progress. This studyfills the gap by comprehensively reviewing related studies on detecting and describing two-dimensional image line segments to provideresearchers with an overall picture and deep understanding. Based on their mechanisms, two taxonomies for line segment detectionand description are presented to introduce, analyze, and summarize these studies, facilitating researchers to learn about them quicklyand extensively. The key issues, core ideas, advantages and disadvantages of existing methods, and their potential applications for eachcategory are analyzed and summarized, including previously unknown findings. The challenges in existing methods and correspondinginsights for potentially solving them are also provided to inspire researchers. In addition, some state-of-the-art line segment detectionand description algorithms are evaluated without bias, and the evaluation code will be publicly available. The theoretical analysis, coupledwith the experimental results, can guide researchers in selecting the best method for their intended vision applications. Finally, this studyprovides insights for potentially interesting future research directions to attract more attention from researchers to this field.

Assessment and Application of Digital Museum Visitors' Emotional Experience Based on Virtual Reality Technology and Emotion Recognition Algorithm

Abstract Each person will have different emotional experience for the same scene or exhibit, which reduces the accuracy of emotional recognition and leads to the complexity of the evaluation of visitors' emotional experience in digital museums. In order to improve the user experience of digital museum, the evaluation and optimization methods of visitors' emotional experience of digital museum based on virtual reality technology and emotion recognition algorithm are studied. The spectrogram is generated according to the voice sent by tourists when they visit the digital museum, which is based on CSWNet_ CRNN emotion recognition depth learning model input, evaluate the tourists' emotional experience results, and draw the tourists' emotional types of digital museums; The visual and auditory features of the digital museum scene with positive emotional experience are extracted respectively. Using virtual reality technology, the extracted features are applied to each link of the digital museum scene content design, optimizing the digital museum virtual reality scene, and improving the digital museum experience. The experiment shows that the tourist emotion recognition accuracy of this method is high, and the emotion recognition accuracy of 300 random tourists can reach 100%. In terms of generating new scenes, the feature extraction results of this scene are consistent with the feature estimation of positive emotions by ordinary people. The use of extracted features to optimize the digital museum scene has better realism and detail accuracy, which can be favored by most people and promote the sustainable development of digital museums.

Map retrieval intention recognition based on relevance feedback and geographic semantic guidance: For better understanding user retrieval demands

Effective retrieval is essential for finding resources in demand handily amidst extensive data records in data warehouse. Mainstream map retrieval methods suffer from intention gap problem and are incapable to describe sophisticated user demands precisely due to the limits of low- and middle-level text or visual feature matching, resulting in unsatisfactory retrieval results. Such limitations are more marked when map retrieval demands were characterized with joint constraints of geographic concepts. To address this issue, we propose a map retrieval intention recognition method to perceive user demands with relevance feedback samples and geographic semantics guidance. Specifically, we construct a hierarchical intention expression model to describe retrieval goals and their multi-dimensional attribute constrains; incorporate geographic ontologies to provide semantic guidance and facilitate recognition; utilize the frequent itemset mining (FIM) algorithm Apriori to generate intention candidates from relevance feedback samples, and search for the optimal intention set by adopting the minimum description length (MDL) principle. The experiments verify the effectiveness of Apriori algorithm and MDL principle on intention recognition. The proposed method outperforms the FIM algorithm Gene Ontology (RuleGO) and the Decision Tree algorithm with Hierarchical Features (DTHF) with higher recognition accuracy and noise tolerance. Furthermore, through our sample augmentation strategy, the method yields promising recognition accuracy even when the feedback sample size is as low as ten, substantially reducing the feedback burden in human-computer interactions. We envision that the application of our method in spatial data infrastructures (SDIs), such as geoportals and catalogue services, could enhance the quality of service and user experience in geospatial data discovery.

Spatiotemporal Properties of Common Semantic Categories for Words and Pictures.

The timing of semantic processing during object recognition in the brain is a topic of ongoing discussion. One way of addressing this question is by applying multivariate pattern analysis to human electrophysiological responses to object images of different semantic categories. However, although multivariate pattern analysis can reveal whether neuronal activity patterns are distinct for different stimulus categories, concerns remain on whether low-level visual features also contribute to the classification results. To circumvent this issue, we applied a cross-decoding approach to magnetoencephalography data from stimuli from two different modalities: images and their corresponding written words. We employed items from three categories and presented them in a randomized order. We show that if the classifier is trained on words, pictures are classified between 150 and 430 msec after stimulus onset, and when training on pictures, words are classified between 225 and 430 msec. The topographical map, identified using a searchlight approach for cross-modal activation in both directions, showed left lateralization, confirming the involvement of linguistic representations. These results point to semantic activation of pictorial stimuli occurring at ≈150 msec, whereas for words, the semantic activation occurs at ≈230 msec.

Study on the relationship between fixation characteristics and hit rate in psychological procedure training of free throw.

Free throw is an important means of scoring in basketball games. With the improvement of basketball competition level and the enhancement of confrontation degree, the number of free throws in the game gradually increases, so the score of free throw will have an important impact on the result of the game. The purpose of this study is to explore the relationship between visual attention characteristics and hit rate of basketball players in free throw psychological procedure training, so as to provide scientific basis for basketball teaching and training. Forty players with similar free throw abilities were randomly assigned to the experimental group (10 males, 10 females) and control group (10 males, 10 females). The experimental group was free throw psychological procedure training, while the control group was trained with routine training, Eye movement indices (number of fixations, fixation duration, and pupil dilation) and the free throw hit rate and analyzed before and after the experiment. Group differences were examined using t-tests, while paired sample t-tests were conducted to compare pre- and post-test results within each group. The training time and training times of the two groups were the same. There were significant differences in fixation duration, number of fixations, pupil diameter and free throw hit rate between pre-test and post-test in the experimental group (P < 0.05). Post-test, there were significant differences in number of fixations, fixation duration, pupil diameter and free throw hit rate between the two groups (P < 0.05). There was a significant positive correlation between number of fixations and free throw hit rate in top (P < 0.01), and there was a significant positive correlation between fixation duration and hit rate in front (P < 0.01). The psychological procedure training can improve the visual information search strategy and information processing ability of free throw, and significantly improve the free throw hit rate. There was a positive correlation between the front fixation time and the free throw hit rate, and there was a positive correlation between the top number of fixations and the free throw hit rate.

Synthetic faces generated with the facial action coding system or deep neural networks improve speech-in-noise perception, but not as much as real faces

The prevalence of synthetic talking faces in both commercial and academic environments is increasing as the technology to generate them grows more powerful and available. While it has long been known that seeing the face of the talker improves human perception of speech-in-noise, recent studies have shown that synthetic talking faces generated by deep neural networks (DNNs) are also able to improve human perception of speech-in-noise. However, in previous studies the benefit provided by DNN synthetic faces was only about half that of real human talkers. We sought to determine whether synthetic talking faces generated by an alternative method would provide a greater perceptual benefit. The facial action coding system (FACS) is a comprehensive system for measuring visually discernible facial movements. Because the action units that comprise FACS are linked to specific muscle groups, synthetic talking faces generated by FACS might have greater verisimilitude than DNN synthetic faces which do not reference an explicit model of the facial musculature. We tested the ability of human observers to identity speech-in-noise accompanied by a blank screen; the real face of the talker; and synthetic talking faces generated either by DNN or FACS. We replicated previous findings of a large benefit for seeing the face of a real talker for speech-in-noise perception and a smaller benefit for DNN synthetic faces. FACS faces also improved perception, but only to the same degree as DNN faces. Analysis at the phoneme level showed that the performance of DNN and FACS faces was particularly poor for phonemes that involve interactions between the teeth and lips, such as /f/, /v/, and /th/. Inspection of single video frames revealed that the characteristic visual features for these phonemes were weak or absent in synthetic faces. Modeling the real vs. synthetic difference showed that increasing the realism of a few phonemes could substantially increase the overall perceptual benefit of synthetic faces.

Cross-modal change detection flood extraction based on self-supervised contrastive pre-training

Abstract. Flood extraction is a critical issue in remote sensing analysis. Accurate flood extraction faces challenges such as complex scenes, image differences across modalities, and a shortage of labeled samples. Traditional supervised deep learning algorithms demonstrate promising prospects in flood extraction. They mostly rely on abundant labeled data. However, in practical applications, there is a scarcity of available labeled samples for flood change regions, leading to an expensive acquisition of such data for flood extraction. In contrast, there is a wealth of unlabeled data in remote sensing images. Self-supervised contrastive learning (SSCL) provides a solution, allowing learning from unlabeled data without explicit labels. Inspired by SSCL, we utilized the open-source CAU-Flood dataset and developed a framework for cross-modal change detection in flood extraction (CMCDFE). We employed the Barlow Twin (BT) SSCL algorithm to learn effective visual feature representations of flood change regions from unlabeled cross-modal bi-temporal remote sensing data. Subsequently, these well-initialized weight parameters were transferred to the task of flood extraction, achieving optimal accuracy. We introduced the improved CS-DeepLabV3+ network for extracting flood change regions from cross-modal bi-temporal remote sensing data, incorporating the CBAM dual attention mechanism. By demonstrating on the CAU-Flood dataset, we proved that fine-tuning with only a pre-trained encoder can surpass widely used ImageNet pre-training methods without additional data. This approach effectively addresses downstream cross-modal change detection flood extraction tasks.

Relevance acquisition through motivational incentives: Modeling the time-course of associative learning and the role of visual features

Abstract Motivational relevance associated with symbolic stimuli impacts both neural and behavioral responses, similar to visual stimuli with inherent emotional valence. However, the specific effects of associated relevance on early sensory stages and lexico-semantic processing of these stimuli remain unclear, particularly considering the role of low-level visual features in relevance acquisition. To address these issues, we employed an associative learning paradigm in which we manipulated visual features, but not the stimuli themselves. The study (N = 48) included a learning phase, where pseudowords were associated with either gain, loss, or neutral outcomes. This was followed by a test phase the next day, involving an old/new decision task, in which stimuli were presented in either the same or a different font. During both phases, pupil responses and event-related brain potentials (P1, Early Posterior Negativity (EPN), Late Positive Complex (LPC), P3) were measured. Stronger pupil responses and increased neural activation in early visual encoding (P1) and lexico-semantic processing (EPN) were observed during relevance acquisition, particularly for loss associations. After relevance acquisition, the most substantial effect on modulating lexico-semantic processing was observed for gain associations, as evidenced by both behavioral responses and neural activity. During the test phase, exposure to incongruent visual features of the stimuli influenced the same processes that were observed during relevance acquisition. Notably, these effects of visual feature congruence were independent of those of associated motivational relevance. These results highlight the dynamic nature of motivational relevance effects, revealing differential effects observed during acquisition and the test phase, as well as between earlier perceptual processing and later neural and behavioral responses.

MVACLNet: A Multimodal Virtual Augmentation Contrastive Learning Network for Rumor Detection

In today’s digital era, rumors spreading on social media threaten societal stability and individuals’ daily lives, especially multimodal rumors. Hence, there is an urgent need for effective multimodal rumor detection methods. However, existing approaches often overlook the insufficient diversity of multimodal samples in feature space and hidden similarities and differences among multimodal samples. To address such challenges, we propose MVACLNet, a Multimodal Virtual Augmentation Contrastive Learning Network. In MVACLNet, we first design a Hierarchical Textual Feature Extraction (HTFE) module to extract comprehensive textual features from multiple perspectives. Then, we fuse the textual and visual features using a modified cross-attention mechanism, which operates from different perspectives at the feature value level, to obtain authentic multimodal feature representations. Following this, we devise a Virtual Augmentation Contrastive Learning (VACL) module as an auxiliary training module. It leverages ground-truth labels and extra-generated virtual multimodal feature representations to enhance contrastive learning, thus helping capture more crucial similarities and differences among multimodal samples. Meanwhile, it performs a Kullback–Leibler (KL) divergence constraint between predicted probability distributions of the virtual multimodal feature representations and their corresponding virtual labels to help extract more content-invariant multimodal features. Finally, the authentic multimodal feature representations are input into a rumor classifier for detection. Experiments on two real-world datasets demonstrate the effectiveness and superiority of MVACLNet on multimodal rumor detection.

Towards determining perceived audience intent for multimodal social media posts using the theory of reasoned action

Increasing use of social media has resulted in many detrimental effects in youth. With very little control over multimodal content consumed on these platforms and the false narratives conveyed by these multimodal social media postings, such platforms often impact the mental well-being of the users. To reduce these negative effects of multimodal social media content, an important step is to understand creators’ intent behind sharing content and to educate their social network of this intent. Towards this goal, we propose Intent-o-meter, a perceived human intent prediction model for multimodal (image and text) social media posts. Intent-o-meter models ideas from psychology and cognitive modeling literature, in addition to using the visual and textual features for an improved perceived intent prediction model. Intent-o-meter leverages Theory of Reasoned Action (TRA) factoring in (i) the creator’s attitude towards sharing a post, and (ii) the social norm or perception towards the multimodal post in determining the creator’s intention. We also introduce Intentgram, a dataset of 55K social media posts scraped from public Instagram profiles. We compare Intent-o-meter with state-of-the-art intent prediction approaches on four perceived intent prediction datasets, Intentonomy, MDID, MET-Meme, and Intentgram. We observe that leveraging TRA in addition to visual and textual features—as opposed to using only the latter–results in improved prediction accuracy by up to 7.5%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$7.5\\%$$\\end{document} in Top-1 accuracy and 8%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$8\\%$$\\end{document} in AUC on Intentgram. In summary, we also develop a web browser application mimicking a popular social media platform and show users social media content overlaid with these intent labels. From our analysis, around 70%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$70\\%$$\\end{document} users confirmed that tagging posts with intent labels helped them become more aware of the content consumed, and they would be open to experimenting with filtering content based on these labels. However, more extensive user evaluation is required to understand how adding such perceived intent labels mitigate the negative effects of social media.

AI-Empowered Multimodal Hierarchical Graph-Based Learning for Situation Awareness on Enhancing Disaster Responses

Situational awareness (SA) is crucial in disaster response, enhancing the understanding of the environment. Social media, with its extensive user base, offers valuable real-time information for such scenarios. Although SA systems excel in extracting disaster-related details from user-generated content, a common limitation in prior approaches is their emphasis on single-modal extraction rather than embracing multi-modalities. This paper proposed a multimodal hierarchical graph-based situational awareness (MHGSA) system for comprehensive disaster event classification. Specifically, the proposed multimodal hierarchical graph contains nodes representing different disaster events and the features of the event nodes are extracted from the corresponding images and acoustic features. The proposed feature extraction modules with multi-branches for vision and audio features provide hierarchical node features for disaster events of different granularities, aiming to build a coarse-granularity classification task to constrain the model and enhance fine-granularity classification. The relationships between different disaster events in multi-modalities are learned by graph convolutional neural networks to enhance the system’s ability to recognize disaster events, thus enabling the system to fuse complex features of vision and audio. Experimental results illustrate the effectiveness of the proposed visual and audio feature extraction modules in single-modal scenarios. Furthermore, the MHGSA successfully fuses visual and audio features, yielding promising results in disaster event classification tasks.

Application of imaging and spectroscopy techniques for grading of bovine embryos - a review

Although embryo transfers have grown considerably in the cattle industry, the selection of embryos required for successful pregnancies remains a challenging task. Visual inspection of 7th-day embryos using a stereomicroscope, followed by classification based on morphological features is the most commonly practiced procedure. However, there are inaccuracies and inconsistencies in the manual grading of bovine embryos. The objective of this review was to evaluate the potential of imaging and spectroscopic techniques in the selection of bovine embryos. Digital analysis of microscopic images through extracting visual features in the embryo region, and classification using machine learning methods have yielded about 88–96% success in pregnancies. The Raman spectral pattern provides valuable information regarding developmental stages and quality of the embryo. The Raman spectroscopy approach has also been successfully used to determine various parameters of bovine oocytes. Besides, Fourier Transform Infrared (FTIR) spectroscopy has the ability to assess embryo quality through analyzing embryo composition, including nucleic acid and amides present. Hyperspectral Imaging has also been used to characterize metabolite production during embryo growth. Although the time-lapse imaging approach is beneficial for morphokinetics evaluation of embryo development, optimized protocols are required for successful implementation in bovine embryo transfers. Most imaging and spectroscopic findings are still only at an experimental stage. Further research is warranted to improve the repeatability and practicality to implement in commercial facilities.

Synthetic Media Analysis Using Deep Learning

In an era characterized by the rapid evolution of digital content creation, synthetic media, particularly deepfake videos, present a formidable challenge to the veracity and integrity of online information. Addressing this challenge requires sophisticated analytical techniques capable of discerning between authentic and manipulated media. This research paper presents a comprehensive study on synthetic media analysis leveraging deep learning methodologies. The suggested method integrates advanced deep learning models, including Convolutional Neural Networks (CNNs) like VGG (Visual Geometry Group), alongside recurrent structures such as LSTM (Long Short-Term Memory). These models are trained and evaluated on a meticulously curated dataset, ensuring diversity and relevance in the synthetic media samples. To facilitate experimentation and reproducibility, the dataset is securely hosted on a reliable platform such as Google Drive. Prior to model training, preprocessing steps including frame extraction are employed to extract essential visual features from the video data. The VGG model serves as a feature extractor, capturing high-level representations of visual content, while the LSTM model learns temporal dependencies and contextual information across frames. Following comprehensive experimentation, the proposed method's ability to detect synthetic media is thoroughly assessed, utilizing metrics such as accuracy. This research contributes to the ongoing discourse on digital media forensics by providing insights into the efficacy of deep learning techniques for synthetic media analysis. The findings underscore the importance of continuous research and development in combating the proliferation of synthetic media, thereby safeguarding the authenticity and trustworthiness of online content. Index Terms—CNN, LSTM, VGG

Low-Level Visual Features of Window Views Contribute to Perceived Naturalness and Mental Health Outcomes

Low-Level Visual Features of Window Views Contribute to Perceived Naturalness and Mental Health Outcomes

Carved Stone Models as Symbolic Features in the Agrarian Landscape of the Sondondo Valley, Ayacucho, Peru

Carved stone models, or piedras maqueta in Spanish are stone representations of the agrarian landscape widely found in the Valley of Sondondo, in the south-central Peruvian Andes. This article aims to examine the representations, locations, scale, and certain visual features of these carved stone models in the valley. My analysis reveals that carved stone monuments provide a unique documentary source of information on the ancient Andean agrarian landscape of the Valley of Sondondo. The piedras maqueta are situated in different areas of the valley, and their study enables an understanding of their symbolism and ritual significance in relation to the environment, as well as daily agricultural spaces and tasks. Therefore, I explore the sacred landscape as an inseparable part of everyday agricultural practices.

133 The effect of sire lines and endpoint cooking temperature on consumer eating experience and visual appeal in pork loin chops

Abstract Prior research and public perception of pork suggest breed influence visual characteristics like color and size and palatability traits like tenderness and flavor of pork chops. Therefore, the objective of this study was to assess consumer perception of visual appeal and palatability of pork loin chops from three sire lines cooked to two different degrees of doneness (DOD). Boneless pork loins from three sire lines (Duroc, Berkshire, and Synthetic) were aged to 14 d post-mortem. Loin quality was then measured, and 2.54-cm chops were frozen from each loin. Chops from each sire line were overwrap-packaged in sets of two and displayed in a retail case with two representative packages per sire line. Panelists rated each package for color, marbling, and size on a just about right (JAR) scale and then indicated their overall liking of each package. Chops from each sire line were cooked to either 63°C or 71°C, cut into 1-cm cubes and two samples from each chop were served to each consumer. Panelists rated 6 samples, each sire line and DOD, for tenderness, flavor, and juiciness on a JAR scale and then rated overall liking. Data were analyzed as a randomized complete block design (RCBD) in SAS 9.4 with panelist age range serving as an influence effect for all panel data. Berkshire chops did not differ (P &amp;gt; 0.08) in redness from other chops, but required 0.13 kg less force to shear, and had a 16.23 cm2 smaller LEA compared with Synthetic and Duroc chops (P &amp;lt; 0.02). Duroc chops had the greatest marbling (P &amp;lt; 0.04) but did not differ in visual color score from other treatments (P &amp;gt; 0.10). There were no differences in consumer visual evaluations (P &amp;gt; 0.45), with traits from all sire lines averaging between 43 and 57 (JAR = 50). Sire line also did not alter visual liking (P = 0.94). There were tendencies for interactions (P &amp;gt; 0.09) between DOD and sire line for consumer sensory evaluation. In general, consumers rated Berkshire chops cooked to 63°C more favorably than other sire line and DOD combinations. Consumers rated chops with different DOD similarly for tenderness and flavor, but tended to score juiciness of chops cooked to 63°C closer to JAR (P = 0.07). Duroc chops were the least tender and least acceptable (P &amp;lt; 0.05). Berkshire chops were juicer than Duroc and Synthetic chops (P &amp;lt; 0.02). Overall, consumers scored all chops between 3.1 and 12.3 units below the JAR line (100-unit scale) regardless of sire line or DOD for tenderness, juiciness, and flavor with overall acceptability within 10 units of neither like nor dislike, which may be indicative of consumers general negative perceptions of pork loin chops.

TB-Net: Intra- and inter-video correlation learning for continuous sign language recognition

Visual feature extraction is the key to continuous sign language recognition (CSLR). However, current CSLR methods based on single-branch networks only rely on intra-video correlation learning to facilitate visual feature optimization. Obviously, this is not conducive to obtaining more robust visual feature representations. Hence, we pioneered a novel CSLR method via intra- and inter-video correlation learning with a two-branch network, named TB-Net. TB-Net explicitly establishes intra-video correlation between glosses and the most relevant video clips at each branch and then introduces inter-video correlation to enhance visual feature extraction at the branch confluence. Specifically, we introduce a contrastive learning-based inter-video correlation module (IEM), which co-optimizes visual features from both branches by designing inter- and intra-video losses to enhance their generalization. In addition, we propose an intra-video correlation module (IAM) based on a gloss-guided attention feature generator to adaptively build mappings between glosses and video clips, which in turn contributes to the acquisition of preliminary gloss-guided visual features within a single video. Extensive experiments on four public CSLR benchmarks show the superior performance of our method.

Towards Retrieval-Augmented Architectures for Image Captioning

The objective of image captioning models is to bridge the gap between the visual and linguistic modalities by generating natural language descriptions that accurately reflect the content of input images. In recent years, researchers have leveraged deep learning-based models and made advances in the extraction of visual features and the design of multimodal connections to tackle this task. This work presents a novel approach towards developing image captioning models that utilize an external k NN memory to improve the generation process. Specifically, we propose two model variants that incorporate a knowledge retriever component that is based on visual similarities, a differentiable encoder to represent input images, and a k NN-augmented language model to predict tokens based on contextual cues and text retrieved from the external memory. We experimentally validate our approach on COCO and nocaps datasets and demonstrate that incorporating an explicit external memory can significantly enhance the quality of captions, especially with a larger retrieval corpus. This work provides valuable insights into retrieval-augmented captioning models and opens up new avenues for improving image captioning at a larger scale.

A novel deep learning based CBIR model using Convolutional Siamese Neural Networks

Content-Based Image Retrieval (CBIR) is a technique that involves retrieving similar images from a large database by analysing the content features of the query image. The heavy usage of digital platforms and devices has in a way promoted CBIR and its allied technologies in computer vision and artificial intelligence. The process entails comparing the representative features of the query image with those of the images in the dataset to rank them for retrieval. Past research was centered around handcrafted feature descriptors based on traditional visual features. But with the advent of deep learning the traditional manual method of feature engineering gave way to automatic feature extraction. In this study, a cascaded network is utilised for CBIR. In the first stage, the model employs multi-modal features from variational autoencoders and super-pixelated image characteristics to narrow down the search space. In the subsequent stage, an end-to-end deep learning network known as a Convolutional Siamese Neural Network (CSNN) is used. The concept of pseudo-labeling is incorporated to categorise images according to their affinity and similarity with the query image. Using this pseudo-supervised learning approach, this network evaluates the similarity between a query image and available image samples. The Siamese network assigns a similarity score to each target image, and those that surpass a predefined threshold are ranked and retrieved. The suggested CBIR system undergoes testing on a widely recognized public dataset: the Oxford dataset and its performance is measured against cutting-edge image retrieval methods. The findings reveal substantial enhancements in retrieval performance in terms of several standard benchmarks such as average precision, average error rate, average false positive rate etc., providing strong support for utilising images from interconnected devices.

Maize leaf disease recognition using PRF-SVM integration: a breakthrough technique

The difficulty of collecting maize leaf lesion characteristics in an environment that undergoes frequent changes, suffers varying illumination from lighting sources, and is influenced by a variety of other factors makes detecting diseases in maize leaves difficult. It is critical to monitor and identify plant leaf diseases during the initial growing period to take suitable preventative measures. In this work, we propose an automated maize leaf disease recognition system constructed using the PRF-SVM model. The PRFSVM model was constructed by combining three powerful components: PSPNet, ResNet50, and Fuzzy Support Vector Machine (Fuzzy SVM). The combination of PSPNet and ResNet50 not only assures that the model can capture delicate visual features but also allows for end-to-end training for smooth integration. Fuzzy SVM is included as a final classification layer to accommodate the inherent fuzziness and uncertainty in real-world image data. Five different maize crop diseases (common rust, southern rust, grey leaf spot, maydis leaf blight, and turcicum leaf blight along with healthy leaves) are selected from the Plant Village dataset for the algorithm’s evaluation. The average accuracy achieved using the proposed method is approximately 96.67%. The PRFSVM model achieves an average accuracy rating of 96.67% and a mAP value of 0.81, demonstrating the efficacy of our approach for detecting and classifying various forms of maize leaf diseases.