Image Content Classification Research Articles

Application of social media communication for museum based on the deep mediatization and artificial intelligence.

Based on deep mediatization theory and artificial intelligence (AI) technology, this study explores the effective improvement of museums' social media communication by applying Convolutional Neural Network (CNN) technology. Firstly, the social media content from four different museums is collected, a dataset containing tens of thousands of images is constructed, and a CNN-based model is designed for automatic identification and classification of image content. The model is trained and tested through a series of experiments, evaluating its performance in enhancing museums' social media communication. Experimental results indicate that the CNN model significantly enhances user participation, access rates, retention rates, and sharing rates of content. Specifically, user participation increased from 15 to 25%, reflecting a 66.7% rise. Content coverage increased from 20 to 35%, showing a 75% increase. User retention rate rose from 10 to 20%, indicating a 100% increase. Content sharing rate increased from 5 to 15%, reflecting a 200% rise. Additionally, the study discusses the model's performance across various museum types, batch sizes, and learning rate settings, verifying its robustness and wide applicability.

BUSINESS INTELLIGENCE BASED RECURRENT NEURAL NETWORK RNN TECHNIQUES FOR SOCIAL MEDIA IMAGE CONTENT CLASSIFICATION

Social media platforms like X and Facebook generate vast amounts of image content daily, necessitating automated methods for classification and analysis. Integrating Business Intelligence (BI) with Recurrent Neural Network (RNN) techniques presents a promising approach to extract valuable insights from this data. This study proposes a methodology for social media image content classification using a hybrid architecture combining Convolutional Neural Networks (CNNs) for feature extraction and RNNs for capturing temporal dependencies. The model is trained on labeled image datasets from X and Facebook, leveraging transfer learning and data augmentation techniques. The contribution lies in the fusion of BI and deep learning techniques, offering a scalable solution for real-time image content classification on social media platforms. This approach enables businesses to streamline marketing analysis, trend detection, and content moderation tasks efficiently. Experimental results demonstrate the effectiveness of the proposed methodology, achieving high accuracy in classifying diverse image content. The model''s performance is validated through comprehensive evaluation metrics, showcasing its robustness and applicability in real-world scenarios.

The Role and Applications of Artificial Intelligence in Image Content Classification

Bu çalışma, yapay zekanın Görüntü içerikli sınıflandırmada oynadığı rolü ve çeşitli sektörlerdeki uygulamalarını sistemli bir yaklaşımla incelemeyi amaçlamaktadır. Literatür taraması ve mantıksal akıl yürütme yöntemlerini birleştirerek yapılan bu araştırma, yapay zekanın Görüntü içerikli sınıflandırmadaki potansiyelini daha iyi anlamayı hedeflemektedir. Bu bağlamda, çalışmanın amacı, yapay zekanın Görüntü içerikli sınıflandırmadaki rolünü vurgulamak ve bu teknolojinin tıp, güvenlik, otomotiv, eğitim ve sanat gibi farklı alanlardaki uygulamalarını açıklamaktır. Literatür taraması ve mantıksal akıl yürütme yöntemleri kullanılarak elde edilen sonuçlar, yapay zekanın Görüntü içerikli sınıflandırmadaki etkisini ve gelecekteki potansiyelini anlamamızı sağlamaktadır. Bu çalışmanın sonuçları, yapay zekanın Görüntü içerikli sınıflandırmadaki önemini vurgulayarak, ilgili alanlarda daha etkili ve verimli çözümler sunma konusundaki yönlendirmelere katkıda bulunmaktadır.

IDENTIFYING COMPLEMENTARY CORNER DETECTORS FOR CORRECT IMAGE PIXELS CLASSIFICATION

Classification of digital image content is mainly done by identifying low level imagefeatures such as corners and edges. The literature shows variety of algorithms for the identification ofcorner and non-corner pixels, important for objects’ identification and image segmentation. However,all of these algorithms produce different results for same data and therefore, suitable for limitedapplications. This paper proposes a hybrid solution of combining complementary corner detectionalgorithms to improve image pixels’ classification. This has been done by identifying the bestdetection algorithm for corner points with small and large angles and producing a hybrid algorithm bycombining the latter two. Results have shown that Harris detector combined with Global and LocalCurvature Points (GLC) improved the detection rate by 28% in synthetic images, but 50% in realimages whereas, the combination of Shi’s detection algorithm with GLC enhanced the detection rateby 25.9% in synthetic images and 123% in real images, showing a significant improvement.

Minor Politics, Major Consequences

Abstract Metadata is part of our knowledge systems and, so, represents and perpetuates political hierarchies and perceptions of relevance. While some of these have come up for scrutiny in the discourses on digitization, some ‘minor’ issues have gone unnoticed and a few new mechanisms of imbalance have escaped attention as well. Yet, all of these, too, influence the usability of digital image collections. This paper traces three fields of ‘minor politics’ and their epistemic consequences, both in general and in particular, with respect to the study of architecture and its visual representation: first, the intrinsic logic of the original collections and their digital representation; second, the role of support staff in the course of digitization and data transfer; and, third, keywording as a matter of disciplinary habitus. It underlines the ‘political’ role of metadata within the context of knowledge production, even on the local level of a single database, and connects to the implementation of contemporary technologies like computer vision and artificial intelligence for image content classification and the creation of metadata. Given the abundance of digitally available (historical) images, image content recognition and the creation of metadata by artificial intelligence are sheer necessities in order to make millions of hitherto unexplored images available for research. At the same time, the challenge to overcome existing colonial and other biases in the training of AI remains. Hence, we are once again tasked to reflect on the delicate criterion of objectivity. The second part of this paper focuses on research done in the ArchiMediaL project (archimedial.eu); it demonstrates both the potentials and the risks of applying artificial intelligence for metadata creation by addressing the three fields mentioned above through the magnifying glass of programming.

Mars Image Content Classification: Three Years of NASA Deployment and Recent Advances

The NASA Planetary Data System hosts millions of images acquired from the planet Mars. To help users quickly find images of interest, we have developed and deployed content-based classification and search capabilities for Mars orbital and surface images. The deployed systems are publicly accessible using the PDS Image Atlas. We describe the process of training, evaluating, calibrating, and deploying updates to two CNN classifiers for images collected by Mars missions. We also report on three years of deployment including usage statistics, lessons learned, and plans for the future.

Content-Aware Eye Tracking for Autostereoscopic 3D Display.

This study develops an eye tracking method for autostereoscopic three-dimensional (3D) display systems for use in various environments. The eye tracking-based autostereoscopic 3D display provides low crosstalk and high-resolution 3D image experience seamlessly without 3D eyeglasses by overcoming the viewing position restriction. However, accurate and fast eye position detection and tracking are still challenging, owing to the various light conditions, camera control, thick eyeglasses, eyeglass sunlight reflection, and limited system resources. This study presents a robust, automated algorithm and relevant systems for accurate and fast detection and tracking of eye pupil centers in 3D with a single visual camera and near-infrared (NIR) light emitting diodes (LEDs). Our proposed eye tracker consists of eye–nose detection, eye–nose shape keypoint alignment, a tracker checker, and tracking with NIR LED on/off control. Eye–nose detection generates facial subregion boxes, including the eyes and nose, which utilize an Error-Based Learning (EBL) method for the selection of the best learnt database (DB). After detection, the eye–nose shape alignment is processed by the Supervised Descent Method (SDM) with Scale-invariant Feature Transform (SIFT). The aligner is content-aware in the sense that corresponding designated aligners are applied based on image content classification, such as the various light conditions and wearing eyeglasses. The conducted experiments on real image DBs yield promising eye detection and tracking outcomes, even in the presence of challenging conditions.

Content-oriented image quality assessment with multi-label SVM classifier

Image content is a fundamental attribute of images and plays an important role in human perception of image information. However, the influence of image content type, which is derived based on the classification of the image content, has been largely ignored in the image quality assessment (IQA). In this paper, a new IQA database based on the classification of image content is built. In particular, the database contains four content types, including landscape, human face, handcrafted scene and the hybrid scene. In total, 80 reference images with 20 images for each type of content are involved, and 1600 distorted images with mean opinion scores (MOSs) are generated by using five types and four levels of distortion. Furthermore, to classify these images, especially for the hybrid case, a Support Vector Machine (SVM) based multi-label (ML) classification is presented. Extensive experiments based on existing no reference IQA (NR-IQA) models show that content classification can greatly facilitate the image quality evaluation. The database and code are made publicly available at: https://github.com/jingchao17/Content-oriented-Database.

Evidence for the incorporation of temporal duration information in human hippocampal long-term memory sequence representations

There has been much interest in how the hippocampus codes time in support of episodic memory. Notably, while rodent hippocampal neurons, including populations in subfield CA1, have been shown to represent the passage of time in the order of seconds between events, there is limited support for a similar mechanism in humans. Specifically, there is no clear evidence that human hippocampal activity during long-term memory processing is sensitive to temporal duration information that spans seconds. To address this gap, we asked participants to first learn short event sequences that varied in image content and interval durations. During fMRI, participants then completed a recognition memory task, as well as a recall phase in which they were required to mentally replay each sequence in as much detail as possible. We found that individual sequences could be classified using activity patterns in the anterior hippocampus during recognition memory. Critically, successful classification was dependent on the conjunction of event content and temporal structure information (with unsuccessful classification of image content or interval duration alone), and further analyses suggested that the most informative voxels resided in the anterior CA1. Additionally, a classifier trained on anterior CA1 recognition data could successfully identify individual sequences from the mental replay data, suggesting that similar activity patterns supported participants' recognition and recall memory. Our findings complement recent rodent hippocampal research, and provide evidence that long-term sequence memory representations in the human hippocampus can reflect duration information in the order of seconds.

Sistem Pengawasan Aktifitas Penggunaan Smartphone Android

Availability the Application Programming Interface (API) for third-party applications on Android devices provides an opportunity to monitor Android devices with each other. This is used to create an application that can facilitate parents in child supervision through Android devices owned. In this study, some features added to the classification of image content on Android devices related to negative content. In this case, researchers using Clarifai API. The result of this research is to produce a system which has feature, give a report of image file contained in target smartphone and can do deletion on the image file, receive browser history report and can directly visit in the application, receive a report of child location and can be directly contacted via this application. This application works well on the Android Lollipop (API Level 22).
 Index Terms— Application Programming Interface(API), Monitoring, Negative Content, Children, Parent.

Building an Image Annotation and Tourism Recommender System

The interest in image annotation and recommendation has been increased due to the ever rising amount of data uploaded to the web. Despite the many efforts undertaken so far, accuracy or efficiency still remain open problems. Here, a complete image annotation and tourism recommender system is proposed. It is based on the probabilistic latent semantic analysis (PLSA) and hypergraph ranking, exploiting the visual attributes of the images and the semantic information found in image tags and geo-tags. In particular, semantic image annotation resorts to the PLSA, exploiting the textual information in image tags. It is further complemented by visual annotation based on visual image content classification. Tourist destinations, strongly related to a query image, are recommended using hypergraph ranking enhanced by enforcing group sparsity constraints. Experiments were conducted on a large image dataset of Greek sites collected from Flickr. The experimental results demonstrate the merits of the proposed model. Semantic image annotation by means of the PLSA has achieved an average precision of 92% at 10% recall. The accuracy of content-based image classification is 82, 6%. An average precision of 92% is measured at 1% recall for tourism recommendation.

The effects of display time and eccentricity on the detection of amplitude and phase degradations in textured stimuli

The amplitude and phase spectra of an image contain important information for perception, and a large body of work has investigated the effects of manipulating these spectra on the recognition or classification of image content. Here, we use a novel means of investigating sensitivity to amplitude and phase spectra properties, testing the ability of observers to detect degradations of the spectral content of synthetic images of textured surfaces that are broadband in the frequency domain. The effects of display time and retinal eccentricity on sensitivity to these two manipulations are compared using stimuli matched for difficulty of detection. We find no difference between the time courses for the detection of degradation in the two spectra; in both cases, accuracy rises above chance when display times are greater than 80 ms. Increasing retinal eccentricity to 8.7°, however, has a significantly stronger effect on the accuracy of detecting degradations of the amplitude spectrum than of the phase spectrum. Further, sensitivity to phase randomization that is restricted to low spatial frequencies is greater in the periphery (at 8.7° eccentricity) than in the fovea. These last two results imply that the fovea and periphery are specialized for the processing of phase spectrum information in distinct spatial frequency bands.

Content-based image quality metric using similarity measure of moment vectors

In this paper, the similarity of moment vectors between the test and the reference image blocks together with the result from the block classification are used in the formulation of an image quality metric (IQM). First, the reference and the test images are divided into non-overlapping 8x8 blocks and transformed into moment domain using Discrete Tchebichef Transform. The moment features are then used in two operations: the local quality index calculation and the image content (block) classification. The local quality index is obtained from the similarity measure of moment vectors between the reference and the test image blocks. Next, the content of each reference image block is classified into three types: ''plain'', ''edge'' and ''texture'', based on its moment energy level and moment energy distribution. The local quality indices obtained from all the image blocks are then averaged based on the block types to obtain three mean quality scores for each test image. The performance of these three mean quality scores and their combinations are studied using the LIVE database. The results show that the performance of the metric is significantly improved by combining the mean quality scores from the edge and texture image region. The best combination (the proposed metric) is then compared with five other IQMs using the LIVE database and four other independent databases. The results show that the proposed metric performs comparatively well for all the databases.

Semantic real-world image classification for image retrieval with fuzzy-ART neural network

Image retrieval based on semantic learning has attracted considerable attention in recent years. Analyzing the contents of an image and retrieving corresponding semantics are important in semantic-based image retrieval systems. Region-based image retrieval systems attempt to reduce the gap between high-level semantics and low-level features by representing images at the object level. In this paper, we apply principal component analysis to extract significant region features and then incorporate them into the proposed two-phase fuzzy adaptive resonance theory neural network (Fuzzy-ARTNN) for real-world image content classification. In general, Fuzzy-ARTNN is an unsupervised classifier. The training patterns in image content analysis are labeled with corresponding categories. This category information is useful for supervised learning. Thus, a supervised learning mechanism is added to label the category of the cluster centers derived by the Fuzzy-ARTNN. Moreover, based on the content analysis by the proposed two-phase Fuzzy-ART, each region in an image is associated with a high-level semantic concept. The proposed system supports both query by keyword(s) with/without region size and query by specified region(s). Experimental results show that the proposed method has high accuracy for semantic-based photograph content analysis and that the results of photograph content analysis are similar to the perception of human eyes. In addition, the semantic-based image retrieval system has a high retrieval rate.

Detection of textured areas in natural images using an indicator based on component counts

An algorithm is presented for the detection of textured areas in natural images. Texture detection has potential application to image enhancement, tone correction, defect detection, content classification, and image segmentation. For example, texture detection may be useful for object detection when combined with color models and other descriptors. Sky, e.g., is generally smooth, and foliage is textured. The texture detector presented here is based on the intuition that texture in a natural image is comprised of many components. The measure we develop examines the structure of local regions of the image. This structural approach enables us to detect both structured and unstructured textures at many scales. Furthermore, it distinguishes between edges and texture, and also between texture and noise. Automatic detection results are shown to match human classification of corresponding image areas.

Hierarchical content classification and script determination for automatic document image processing

Page segmentation and image content classification play an important role in automatic image processing with applications to mixed-type document image compression, form and check reading, and automatic mail sorting. In this paper, we first present an enhanced background thinning based approach for fast page segmentation. After the analysis of three different methods individually, a hierarchical approach for document content classification is proposed, which classifies a sub-image into one of two categories: text and halftone. Our approach combines a neural network model, cross-correlation metric, and Kolmogorov complexity measure in a hierarchical structure. Considering the necessity of a recognition system, we also propose using a three-layer feedforward neural network to classify text regions into Chinese and English scripts. The classification accuracy on a number of document images reaches 100% and 97.1% for halftone region and text region, respectively. Meanwhile, the system can achieve a correct rate of 92.3% and 95.0% for Chinese and alphabetic script determination, respectively.