Image Gallery Research Articles

Tell Me What Is Good about This Property: Leveraging Reviews for Segment-Personalized Image Collection Summarization

Image collection summarization techniques aim to present a compact representation of an image gallery through a carefully selected subset of images that captures its semantic content. When it comes to web content, however, the ideal selection can vary based on the user's specific intentions and preferences. This is particularly relevant at Booking.com, where presenting properties and their visual summaries that align with users' expectations is crucial. To address this challenge, in this work, we consider user intentions in the summarization of property visuals by analyzing property reviews and extracting the most significant aspects mentioned by users. By incorporating the insights from reviews in our visual summaries, we enhance the summaries by presenting the relevant content to a user. Moreover, we achieve it without the need for costly annotations. Our experiments, including human perceptual studies, demonstrate the superiority of our cross-modal approach, which we coin as CrossSummarizer over the no-personalization and image-based clustering baselines.

Updated distribution of Zoropsis spinimana (Dufour, 1820; Araneae: Zoropsidae) in Germany and novel insights into its ecology based on a citizen science survey

In Germany, Zoropsis spinimana (Dufour, 1820) is an introduced, likely synanthropic spider species. Here, we report the results of a nationwide mapping appeal conducted by the citizen science platform NABU-naturgucker.de, used to assemble live distributional data for the species in Germany. With the help of media interest in this species, we gathered a valuable dataset and a large image gallery of the species. In just five weeks, we received more than 15,000 records, representing a 2.3-fold increase in occupied territory compared to previous knowledge. By analyzing the data in detail, we obtained novel insights into the ecology and eco-geography of Z. spinimana in Germany, including information on prey, coloration, potential predators, altitudinal distribution and temporal appearance, along with two cases of accidental human translocation.

Image-Specific Information Suppression and Implicit Local Alignment for Text-Based Person Search.

Text-based person search (TBPS) is a challenging task that aims to search pedestrian images with the same identity from an image gallery given a query text. In recent years, TBPS has made remarkable progress, and state-of-the-art (SOTA) methods achieve superior performance by learning local fine-grained correspondence between images and texts. However, most existing methods rely on explicitly generated local parts to model fine-grained correspondence between modalities, which is unreliable due to the lack of contextual information or the potential introduction of noise. Moreover, the existing methods seldom consider the information inequality problem between modalities caused by image-specific information. To address these limitations, we propose an efficient joint multilevel alignment network (MANet) for TBPS, which can learn aligned image/text feature representations between modalities at multiple levels, and realize fast and effective person search. Specifically, we first design an image-specific information suppression (ISS) module, which suppresses image background and environmental factors by relation-guided localization (RGL) and channel attention filtration (CAF), respectively. This module effectively alleviates the information inequality problem and realizes the alignment of information volume between images and texts. Second, we propose an implicit local alignment (ILA) module to adaptively aggregate all pixel/word features of image/text to a set of modality-shared semantic topic centers and implicitly learn the local fine-grained correspondence between modalities without additional supervision and cross-modal interactions. Also, a global alignment (GA) is introduced as a supplement to the local perspective. The cooperation of global and local alignment modules enables better semantic alignment between modalities. Extensive experiments on multiple databases demonstrate the effectiveness and superiority of our MANet.

Prototype-guided Attention Distillation for Discriminative Person Search.

Person search aims to localize a person of interest in a large image gallery captured by multiple, non-overlapping cameras. Prevalent unified methods have suffered from (1) noisy proposals with mis-detection and occlusion, and (2) large appearance variation within a class, which deteriorates the prototype-based metric learning. To address these problems, we introduce a Prototype-guided Attention Distillation, shortly PAD, which exploits a prototype (a typical representation of an identity) as a guidance to the attention module to consistently highlight identity-inherent regions across different poses. To utilize the knowledge encoded in prototypes for matching unseen IDs, PAD conducts attention distillation to guide student Re-ID queries by deeply mimicking attention maps from the prototype query. Additionally, to address large intra-class variation induced by pose or camera views, we extend PAD with multiple part prototypes representing consistent local regions across different instances. Furthermore, we exploit an adaptive momentum strategy for robust attention distillation in PAD to update more distinct prototypes. Extensive experiments conducted on CUHK-SYSU and PRW demonstrate the effectiveness of PAD, showcasing state-of-the-art performance. Moreover, our distilled attention surprisingly highlights distinguished multiple regions for person search.

Multi-level Salient Feature Mining Network for Person Re-identification

Person re-identification (Re-ID) algorithms can retrieve the same pedestrian’s images from an image gallery captured by multiple cameras when given a pedestrian image. Due to changes in pedestrian postures, illuminations, and perspectives, it remains a significant challenge to improve the accuracy of person re-identification. Although the attention mechanism can alleviate some of these issues, it causes attention-based methods to pay excessive attention to features in the most salient areas of images while ignoring discriminant features outside the most salient areas, resulting in the insufficient discriminability of features extracted by attention-based methods. For this purpose, we propose a Multi-level Salient Feature Mining Network (MSFM-Net). First, by embedding attention modules in ResNet50, the model extract the most salient pedestrian feature maps. Second, the model uses two sub-salient feature mining branches to extract the second-level and third-level salient feature maps (collectively referred to as sub-salient feature maps). Third, the model uses the feature maps fusion module to combine the most salient feature maps with sub-salient feature maps to obtain the fused salient feature maps. Finally, the model pools the fused salient feature maps to produce more discriminant pedestrian representations. The results of two benchmark datasets demonstrate that MSFM-Nets performance reaches the current advanced level.

A dual track deep fusion network for citrus disease classification using group shuffle depthwise feature pyramid and Swin transformer

Citrus fruits are globally significant crops, valued for their unique taste, nutritional value, and versatility in culinary applications. However, these crops are vulnerable to diseases that can substantially reduce their yield and quality, leading to significant financial losses. It is crucial to efficiently identify and manage diseases affecting citrus fruits to ensure food security and support sustainable agriculture. Traditionally, identifying symptoms associated with these diseases requires expert scientific and observational skills. With the advent of deep learning methods, it has become possible to automatically identify disease patterns from images of affected plants. In this research, a novel dual branch deep-learning network was proposed for the classification of citrus plant diseases. The Group Shuffle Depthwise Feature Pyramid (GSDFP), which constitutes the first branch, utilizes convolution blocks to extract local features. The second branch leverages the Swin transformer to integrate global contextual awareness into the extracted features and facilitates the learning process through long-term dependencies. Subsequently, the features from the two branches are fused and passed through a shuffle attention module, effectively capturing contextual relationships between them. The effectiveness of the proposed approach was validated on two benchmark datasets, namely the Citrus Plant Dataset and the Citrus Disease Image Gallery Dataset. The proposed network obtained a classification accuracy of 98.19%.

Juxtaform: interactive visual summarization for exploratory shape design

We present juxtaform , a novel approach to the interactive summarization of large shape collections for conceptual shape design. We conduct a formative study to ascertain design goals for creative shape exploration tools. Motivated by a mathematical formulation of these design goals, juxtaform integrates the exploration, analysis, selection, and refinement of large shape collections to support an interactive divergence-convergence shape design workflow. We exploit sparse, segmented sketch-stroke visual abstractions of shape and a novel visual summarization algorithm to balance the needs of shape understanding, in-situ shape juxtaposition, and visual clutter. Our evaluation is three-fold: we show that existing shape and stroke clustering algorithms do not address our design goals compared to our proposed shape corpus summarization algorithm; we compare juxtaform against a structured image gallery interface for various shape design and analysis tasks; and we present multiple compelling 2D/3D applications using juxtaform.

Diagnosis Method of Decay-like Composite Insulators in a High-Humidity Environment Based on Characteristic Coefficient of Temperature Rise Gradient.

At present, the temperature rise in insulators is observed using infrared thermometry as a common method of diagnosing decay-like insulators. However, the original characteristic data obtained by infrared thermometry cannot effectively distinguish some of the decay-like insulators from those with ageing sheaths. Therefore, it is imperative to find a new diagnostic characteristic quantity. Based on statistical data, this article first explains that existing diagnostic methods have limited diagnostic effectiveness and a high false detection rate for insulators in a slightly heated state. A full-scale temperature rise test is carried out on a batch of composite insulators returned from the field under high-humidity conditions. Two different defective insulators with similar temperature rise profiles are identified, and an electro-thermal coupling simulation model is developed based on the dielectric characteristic parameters of the above insulators for both core rod defects and sheath ageing. A new infrared diagnostic feature, the temperature rise gradient coefficient, is then obtained to identify the source of abnormal heat in insulators using statistical analysis of an infrared image gallery of abnormally hot composite insulators obtained from field inspections and laboratory tests.

IDphy: An International Online Resource for Molecular and Morphological Identification of Phytophthora.

Phytophthora, with 203 species, is a genus of high importance in agriculture worldwide. Here, we present the online resource "IDphy", developed to facilitate the correct identification of species of Phytophthora using the type specimens from the original descriptions wherever possible. IDphy emphasizes species of high economic impact and regulatory concern for the United States. IDphy presents an interactive Lucid key and a tabular key for 161 culturable species described as of May 2018, including 141 ex-types and 20 well-authenticated specimens. IDphy contains standard operating procedures for morphological and molecular characterization, as well as a glossary, image gallery, and numerous links. Each of the 161 factsheets includes access to nomenclature and morphological and molecular features, including sequences of the internal transcribed spacer ribosomal DNA, cytochrome C oxidase subunit I (barcoding genes), YPT1, β-tubulin, elongation factor 1a, L10, heat shock protein 90, and other genes. IDphy contains an innovative in silico BLAST and phylogenetic sequence analysis using NCBI. The IDphy mobile app, released in August 2021 (free for Android or iOS), allows users to take the Lucid key into the laboratory. IDphy is the first online identification tool based on the ex-types implemented for plant pathogens. In this article, we also include information for 21 new species and one hybrid described after the publication of IDphy, the status of the specimens of the types and ex-types at international herbaria and culture collections, and the status of genomes at the GenBank (currently 153 genome assemblies which correspond to 42 described species, including 16 ex-types). The effectiveness of the IDphy online resource and the content of this article could inspire other researchers to develop additional identification tools for other important groups of plant pathogens.

Phylogeny, diversity and biogeography of flightless amphi-Pacific lymantine weevils (Coleoptera: Curculionidae: Molytinae)

We use DNA sequence data to generate the first phylogenetic hypothesis for the weevil tribe Lymantini. These are leaf litter inhabiting beetles generally regarded as restricted to the New World and taxonomically arranged in two subtribes, 11 genera and some 150 named species. An additional genus of questionable affinities to the tribe, Devernodes Grebennikov, 2018, has five described species in Southeastern Asia. All these beetles are flightless and some have eyes reduced in size or absent, traits normally associated with limited dispersal capacity. We performed a phylogenetic analysis of 153 terminals (50 of them belong to Lymantini representing Devernodes and all but three named genera) based on 4,174 bp alignment of one mitochondrial (cox1) and two nuclear fragments (ITS2 and 28S). We find that both Lymantini subtribes Lymantina and Caecossonina are monophyletic, the latter sister to the amphi-Atlantic tribe Anchonini. The Asian genus Devernodes is deeply nested among American Lymantina. The clade of Anchonini plus Lymantini is consistently recovered outside of the CCCMS clade of “higher” weevils (Curculioninae, Conoderinae, Cossoninae, Molytinae and Scolytinae). We hypothesize that the polished head capsule of adult beetles is an apomorphy of Anchonini and Lymantini, the 8-segmented antennal funicle is an apomorphy of Anchonini plus Caecossonina. We attribute the origin of the currently observed amphi-Pacific distribution of Lymantina to normal ecological dispersal facilitated by the warmer periods of the Cenozoic such as the Eocene, and by presently submerged Arctic land bridges. Using parsimony we hypothesize a North American origin for the Anchonini plus Lymantini crown group, as well as that of Lymantina. We argue that Bronchotibia adunatus Poinar & Legalov, 2021, a Dominican amber adult weevil fossil, is not a member of Lymantini and re-classify it as Curculionidae incertae sedis. We present an image gallery of 28 Lymantini specimens to document the morphological diversity of the tribe. We hypothesize the existence of unnamed American genera of Lymantina and make public the DNA-barcode dataset of 89 Lymantini specimens.

Rhino Resource Center: accessing and utilizing a unique digital database

Rhinos have a long history in European art and literature, having captivated the public for over 500 years. In China and India, this is even longer (Bishop 1933, Bose 2020). This long-term record means that there is a wealth of rhino imagery and publications available for researchers. The Rhino Resource Center (RRC) is a repository of such information. Whilst the power of the RRC’s literature database has been documented, we consider that the image gallery has thus far been underappreciated by researchers. To complement a recent publication that made use of the images in the RRC to investigate changing human perceptions of rhinos and morphological changes in rhinos over time, we describe the volume and type of data available on the RRC and the images it contains and how to access these, including thematic information, time and location data, ecological data and morphology. We hope that this paper will facilitate rhino researchers making greater use of this information and we strongly encourage uptake of this resource for future rhino research.

Clothes retrieval based on ResNet and cluster triplet loss

Clothes image retrieval is a task that retrieves exact or very similar clothes from a large clothes image gallery according to a given query image. The high deformability of clothes and similar designs make clothes retrieval very difficult. Convolutional neural networks perform well in the feature extraction field, but cannot extract sufficiently fine-grained features for clothes retrieval tasks. Therefore, a model based on ResNet was designed in this article, with several improvements made to it to ensure better feature discrimination. On the other hand, pair-based loss functions that are commonly used in image retrieval suffer from insufficient discrimination ability, difficulty in convergence, and time-consuming during training. Cluster triplet loss function is proposed in this article, which reduces the computational complexity in the training phase and enables the model to have a certain ability to resist noise labels. Experiments are completed on In-shop clothes retrieval benchmark and Consumer-to-shop clothes retrieval benchmark in Deepfashion, and stanford online products (SOP) to supplement. Our method can have Recall@1 improvements of 1.3–1.7% on all three datasets, surpassing the latest state-of-the-art methods.

Practical Examples of Cooperation and of Visualizing Species Information: Species identification and invasive alien species (IAS)

The SLU Swedish Species Information Centre (SSIC, SLU Artdatabanken) accumulates, analyses and disseminates information concerning species and habitats occurring in Sweden. The work is frequently carried out in cooperation with various experts and non-governmental organisations. The SSIC produces the Swedish Red List of threatened species and works on commission by the Government and other authorities within the field of Swedish biodiversity, e.g. risk assessment of invasive alien species (IAS) and the Habitats Directive. Since 2002, when the Swedish Taxonomy Initiative (STI) was established, the SSIC is commissioned by the Swedish Parliament to identify and describe all species of multicellular plants, fungi and animals in Sweden and to make the information available to scientists, conservationists and the public. The SSIC provides an open access biodiversity reporting and analysis infrastructure including for example the Swedish Species Observation System, the Swedish taxonomic backbone Dyntaxa and tools for species information including traits, terminology and species identification (artfakta.se). All systems, including the Swedish LifeWatch (SLW) Analysis Portal, rely on recognized standards to ensure interoperability and consist of databases, APIs and web applications. In addition, the SSIC was the leading partner within the former SLW consortium and now cooperates in the Swedish Biodiversity Data Infrastructure and the Living Atlases Community. In the first example we show what data from Hygrophorus, one of the genera where taxon concepts are mapped within Fennoscandia, is available in the SSIC web applications and hence what kind of data could be shared, based on the ongoing collaboration within the Nordic Taxonomy Initiatives. The Hygrophorus data consists of a identification key, descriptions (characters, distribution, ecology), names & classification, nature conservation lists (for red listed species descriptions of threats, conservation measures and assessments). Furtheremore, there are different classfications for filter functions (e.g. landscape type, habitat, substrate, ecological group, mycorrhiza/symbiosis), an image gallery and observation maps generated from sightings. Most come from Artportalen, which in total contains more than 93,000,000 georeferenced observations, along with 3,000,000 images, videos or sounds of some 37,000 species from Sweden. Observation records are harvested by the national Species Observation System (SOS) (serving, amongst others, the Swedish authorities with data) and by the Global Biodiversity Information Facility (GBIF). The SSIC also offers tool for image-based species identification, collaborating with PlantNet and Artsoraklet/Naturalis. The second example demonstrates the Swedish network and collaboration on invasive alien species (IAS), which is another example of species information of international interest when it comes to sharing and vizualiazing data. In collaboration with the Swedish Environmental Protection Agency (Naturvårdsverket) and the Swedish Agency for Marine and Water Management (Havs- och vattenmyndigheten) a reporting system focusing firstly on the species listed in the IAS Regulation 1143/2014 (EU) has been developed in Sweden (invasivaarter.nu). Here, anyone can report an observation of one of these species, with information on geographic position, date and a photo. This information will be directed to a network of experts verifying both the species identification and the place, if necessary in dialogue with the reporting individual. Once verified occurrence data is published, responsible authorities (often County Administrative Boards, CABs) can carry out field checks and, if necessary, instigate appropriate eradication measures. A digital catalogue of such eradication methods is under development and collaboration between IAS coordinators in regional CABs is being established. The CABs have a separate tool for documentation of eradication measures and since the beginning of this year the complete digital chain is working, from observation to documentation of eradication measures taken. If/when measures are carried out (by CABs) this information is fed back from the CAB-tool into the database in SSIC where it is possible to search for and visualize this information.

Efficient and accurate diagnosis of otomycosis using an ensemble deep-learning model.

Otomycosis accounts for over 15% of cases of external otitis worldwide. It is common in humid regions and Chinese cultures with ear-cleaning custom. Aspergillus and Candida are the major pathogens causing long-term infection. Early endoscopic and microbiological examinations, performed by otologists and microbiologists, respectively, are important for the appropriate medical treatment of otomycosis. The deep-learning model is a novel automatic diagnostic program that provides quick and accurate diagnoses using a large database of images acquired in clinical settings. The aim of the present study was to introduce a machine-learning model to accurately and quickly diagnose otomycosis caused by Aspergillus and Candida. We propose a computer-aided decision-making system based on a deep-learning model comprising two subsystems: Java web application and image classification. The web application subsystem provides a user-friendly webpage to collect consulted images and display the calculation results. The image classification subsystem mainly trained neural network models for end-to-end data inference. The end user uploads a few images obtained with the ear endoscope, and the system returns the classification results to the user in the form of category probability values. To accurately diagnose otomycosis, we used otoendoscopic images and fungal culture secretion. Fungal fluorescence, culture, and DNA sequencing were performed to confirm the pathogens Aspergillus or Candida spp. In addition, impacted cerumen, external otitis, and normal external auditory canal endoscopic images were retained for reference. We merged these four types of images into an otoendoscopic image gallery. To achieve better accuracy and generalization abilities after model-training, we selected 2,182 of approximately 4,000 ear endoscopic images as training samples and 475 as validation samples. After selecting the deep neural network models, we tested the ResNet, SENet, and EfficientNet neural network models with different numbers of layers. Considering the accuracy and operation speed, we finally chose the EfficientNetB6 model, and the probability values of the four categories of otomycosis, impacted cerumen, external otitis, and normal cases were outputted. After multiple model training iterations, the average accuracy of the overall validation sample reached 92.42%. The results suggest that the system could be used as a reference for general practitioners to obtain more accurate diagnoses of otomycosis.

MFHI: Taking Modality-Free Human Identification as Zero-Shot Learning

Human identification is an important topic in event detection, person tracking, and public security. There have been numerous methods proposed for human identification, such as face identification, person re-identification, and gait identification. Typically, existing methods predominantly classify a queried image to a specific identity in an image gallery set (I2I). This is seriously limited for the scenario where only a textual description of the query or an attribute gallery set is available in a wide range of video surveillance applications (A2I or I2A). However, very few efforts have been devoted towards modality-free identification, i.e., identifying a query in a gallery set in a scalable way. In this work, we take an initial attempt, and formulate such a novel Modality-Free Human Identification (named MFHI) task as a generic zero-shot learning model in a scalable way. Meanwhile, it is capable of bridging the visual and semantic modalities by learning a discriminative prototype of each identity. In addition, the semantics-guided spatial attention is enforced on visual modality to obtain representations with both high global category-level and local attribute-level discrimination. Finally, we design and conduct an extensive group of experiments on two common challenging identification tasks, including face identification and person re-identification, demonstrating that our method outperforms a wide variety of state-of-the-art methods on modality-free human identification.

Zero-shot sketch-based image retrieval with structure-aware asymmetric disentanglement

The goal of Sketch-Based Image Retrieval (SBIR) is using free-hand sketches to retrieve images of the same category from a natural image gallery. However, SBIR requires all test categories to be seen during training, which cannot be guaranteed in real-world applications. So we investigate more challenging Zero-Shot SBIR (ZS-SBIR), in which test categories do not appear in the training stage. After realizing that sketches mainly contain structure information while images contain additional appearance information, we attempt to achieve structure-aware retrieval via asymmetric disentanglement. For this purpose, we propose our STRucture-aware Asymmetric Disentanglement (STRAD) method, in which image features are disentangled into structure features and appearance features while sketch features are only projected to structure space. Through disentangling structure and appearance space, bi-directional domain translation is performed between the sketch domain and the image domain. Extensive experiments demonstrate that our STRAD method remarkably outperforms state-of-the-art methods on three large-scale benchmark datasets.

William Morris, News from Nowhere, and the Hammersmith Bridge: Visual Encounters

Abstract This article discusses a central symbol in William Morris’ News from Nowhere (1890): that of the ‘bridge’. I argue that Morris’ use of the Hammersmith Suspension Bridge as a ‘gateway’ to his imagined journey up the river Thames to Lechlade was stimulated by the alterations and rebuilding work carried out on the bridge in the 1880s. The article traces the debates and developments surrounding the bridge during the period when Morris had moved into his Hammersmith house (Guest’s lodgings at the start of the novel), which enjoyed a view of the bridge from its windows. In response to the industrial bridge architecture of the nineteenth century, Morris describes a future where the bridges of the Thames have returned to something like the medieval models he espouses. The article follows the route of Guest’s Thames boat journey in Nowhere, describing the contemporary bridges Morris mentions in the text, and considering aspects of the transformation or disappearance of these in Nowhere. As Guest reaches the Upper Thames, his future world and the present day merge in the descriptions of the medieval bridges that survive in both his own day and the utopian future, providing an ironic condemnation of the capitalist Victorian architecture and an oblique argument for conservation concerns. The metaphor of the ‘bridge’ becomes crucial to his political position, bridging his medieval idealism to his modern socialism. The essay provides an image gallery of the bridges in the novel.

Classification of Citrus Diseases Using Optimization Deep Learning Approach.

Most plant diseases have apparent signs, and today's recognized method is for an expert plant pathologist to identify the disease by looking at infected plant leaves using a microscope. The fact is that manually diagnosing diseases is time consuming and that the effectiveness of the diagnosis is related to the pathologist's talents, making this a great application area for computer-aided diagnostic systems. The proposed work describes an approach for detecting and classifying diseases in citrus plants using deep learning and image processing. The main cause of decreased productivity is considered to be plant diseases, which results in financial losses. Citrus is an important source of nutrients such as vitamin C all around the world. On the contrary, citrus diseases have a negative impact on the citrus fruit and quality. In the recent decade, computer vision and image processing techniques have become increasingly popular for the detection and classification of plant diseases. The suggested approach is evaluated on the citrus disease image gallery dataset and the combined dataset (citrus image datasets of infested scale and plant village). These datasets were used to identify and classify citrus diseases such as anthracnose, black spot, canker, scab, greening, and melanose. AlexNet and VGG19 are two kinds of convolutional neural networks that were used to build and test the proposed approach. The system's total performance reached 94% at its best. The proposed approach outperforms the existing methods.

Modeling user preferences in online stores based on user mouse behavior on page elements

PurposeOnline businesses require a deep understanding of their customers’ interests to innovate and develop new products and services. Users, on the other hand, rarely express their interests explicitly. The purpose of this study is to predict users’ implicit interest in products of an online store based on their mouse behavior through various product page elements.Design/methodology/approachFirst, user mouse behavior data is collected throughout an online store website. Next, several mouse behavioral features on the product pages elements are extracted and finally, several models are extracted using machine learning techniques to predict a user’s interest in a product.FindingsThe results indicate that focusing on mouse behavior on various page elements improves user preference prediction accuracy compared to other available methods.Research limitations/implicationsUser mouse behavior was used to predict consumer preferences in this study, therefore gathering additional data on user demography, personality dimensions and emotions may significantly aid in accurate prediction.Originality/valueMouse behavior is the most repeated behavior during Web page browsing through personal computers and laptops. It has been referred to as implicit feedback in some studies and an effective way to ascertain user preference. In these studies, mouse behavior is only assessed throughout the entire Web page, lacking a focus on different page elements. It is assumed that in online stores, user interaction with key elements of a product page, such as an image gallery, user reviews, a description and features and specifications, can be highly informative and aid in determining the user’s interest in that product.

A Visual Dashboard of Thai Weaving Pattern Image Gallery

The knowledge of weaving pattern design has spread to almost all regions in Thailand since prehistoric. The ethnic pattern design may vary based on ideology, beliefs, and traditions of people in each region. Until this day, the original weaving pattern may change by the adaptation of local textile designer and the Cross-cultural environment. Thus, the study of Thai weaving pattern design is necessary for reserving Thai ethnic wisdom of textile design. This work aims to develop the data dashboard by collecting Thai weaving pattern images from Google based on the information from the Royal Thai Silk Conservation Village Project, the Office of Sericulture Conservation and Standard Conformity Assessment, the Queen Sirikit Department of Sericulture, Ministry of Agriculture and Cooperatives. Our image gallery of Thai weaving patterns consists of 28 patterns from 4 regions: 1) The weaving patterns of northern Thailand include Jok Mae Jam, Jok Maueng Long, Yok Mook Lab Lae, Yok Dok Lamphun silk, and Sin Maueng Nan; 2) The weaving patterns of northeastern Thailand include Hol, Hang Kra Rok, Amprom, La Buek, Sin Tiew, Saked silk, Kaab Bua, Mud Mee Chonnabot silk, Luk Kaew patterned ebony-dyed silk, Mud Mee Teen Dang, Mud Mee Kaew Mookda silk, Mud Mee Kor Naree silk, Praewa silk, Mud Mee Soi Dok Mak silk, Samor, Anloonseem, and Kid; 3) The weaving patterns of central Thailand include Thai-Yuan Yok Mook and Thai-Yuan Jok; The weaving patterns of southern Thailand include Chuan Tani, Pum Riang silk, Yok Maueng Nakhon, and Na Muen Si. Our system can show the useful information of Thai weaving pattern such as the location on the map, the philosopher who gave the detail of weaving pattern, the weaving pattern design, and the textile techniques.