There are currently different efforts to use Supervised Neural Networks (NN) to automatically label damages on images of above ground infrastructure (buildings made of concrete) taken after an earthquake. The goal of the supervised NN is to classify raw input data according to the patterns learned from an input training set. This input training data set is usually supplied by experts in the field, and in the case of this project, structural engineers carefully and mostly manually label these images for different types of damage. The level of expertise of the professionals labeling the training set varies widely, and some data sets contain pictures that different people have labeled in different ways when in reality the label should have been the same. Therefore, we need to get several experts to evaluate the same data set; the bigger the ground truth/training set the more accurate the NN classifier will be. To evaluate these variations among experts, which can be considered equal to the task of evaluating the quality of the expert, using probabilistic theory we first need to implement a tool able to compare different images classified by different experts and apply a certainty level to the experts tagged labels. This master's thesis implements this comparative tool. We also decided to implement the comparative tool using parallel programming paradigms since we foresee that it will be used to train multiple young engineering students/professionals or even novice citizen volunteers (“trainees”) during after-earthquake meetings and workshops. The implementation of this software tool involves selecting around 200 photographs tagged by an expert with proven accuracy (“ground truth”) and comparing them to files tagged by the trainees. The trainees are then provided with instantaneous feedback on the accuracy of their damage assessment. The aforementioned problem of evaluating trainee results against the expert is not as simple as comparing and finding differences between two sets of image files. We anticipate challenges in that each trainee will select a slightly different sized area for the same occurrence of damage, and some damage-structure pairs are more difficult to recognize and tag. Results show that we can compare 500 files in 1.5 seconds which is an improvement of 2x faster compared to sequential implementation.

Šio projekto tikslas sukurti programinę įrangą, kuri padės muziejines vertybes padaryti prieinamas plačiajai visuomenei. Sistemos veikimo principas paremtas atviro turinio pildymu. Didėjant interneto vartotojų skaičiui, augant informacijos kiekiams internete, tampa sunku ir per brangu vienam žmogui ar organizacijai aprašyti, kategorizuoti ar kitaip apibūdinti informaciją. Todėl atsirado nauja metodologija pavadinta anglišku terminu „Folksonomy“,kurį lietuviškai būtų galima pavadinti žmonių taksonomija. Tai yra informacijos internete viešas pateikimas, naudojant visiškai neapribotus ir nesuvaržytus apibūdinimus. Taip gali būti aprašytos internetinės svetainės, nuotraukos, nuorodos.„Folksnonomy“ tikriausiai yra priešingybė taksonomijai, kur aprašymo sistemos autoriai dažniausiai yra turinio kurį aprašo autoriai. Aprašymai yra bendrai vadinami žymėmis (Tags), o aprašymo procesas – žymėjimas (tagging). Šio žymėjimo tikslas yra padaryti informaciją lengviau randamą, lengviau išaiškinamą ir lengviau skleidžiamą internete. Žmonių taksonomija atsirado internetinių bendruomenių pagrindu.Jos sukūrė galimybę Interneto vartotojams aprašyti ir pasidalinti pačių sukurtu turiniu, pavyzdžiui: fotografijomis, interneto svetainių, knygų katalogais. Darbe nagrinėjama tyrimo sritis susijusi su paveikslų žymėjimo sistemų projektavimu ir galimų projektavimo metodų parinkimu. Analitinėje darbo dalyje pristatomos panašios sistemos, jos lyginamos. Projektinėje dalyje pateikiama suprojektuotos... [toliau žr. visą tekstą] / The main objective of this project was to design and develop open source software for image tagging. During this process the analysis of alternative image tagging systems was performed, as well as gathering system requirements. When the system was developed, there was made a research to determine software quality. There was made three types of researches: research of software functionality, research of software quality to fit ISO 9126 standard and research of using DB objects. Finally it was determined that software for image tagging fits most functional requirements. Also It fits ISO 9126 quality standards. And using DB objects was justifiable and suitable for this kind of system.

This thesis investigates efficient manual image tagging approaches. It specifically focuses on organising images into clusters depending on their content, and thus on simplifying the selection of similar photos. Such selections may be efficiently tagged with common tags. The thesis investigates known techniques for visualisation of image collections according to the image content, together with dimensionality reduction methods. The most suitable methods are considered and evaluated. The thesis proposes a novel method for presenting image collections on 2D displays which combines a timeline with similarity grouping (Timeline projection). This method utilizes t-Distributed Stochastic Neighbour Embedding (t-SNE) for otpimally projecting groupings in high dimensional feature spaces onto the low-dimensional screen. Various modifications of t-SNE and ways to combine it with the timeline are discussed and chosen combination is implemented as a web interface and is qualitatively evaluated in a user study. Possible directions of further research on the subject are suggested.

生活於數位時代，巨量的個人生命記憶使得人們難以輕易解讀，必須經過檢索或標籤化才可以進一步瞭解背後的意涵。本研究著力個人記憶裡繁瑣及週期性的廣泛事件，進行於「情節記憶語意化」以及「何以權衡大眾與個人資訊」兩議題之探討。透過生命記憶平台裡影像標籤自動化功能，我們以時空資訊為索引提出持續性概念重構模型，整合共同知識、個人近況以及個人偏好三項因素，模擬人們對每張照片下標籤時的認知歷程，改善其廣泛事件上註釋困難。在實驗設計上，實作大眾資訊模型、個人資訊模型以及本研究持續性概念重構模型，並招收九位受試者來剖析其認知歷程以及註釋效率。實驗結果顯示持續性概念重構模型解決了上述大眾與個人兩模型上的極限，即舊地重遊、季節性活動、非延續性活動性質以及資訊邊界註釋上的問題，因此本研究達成其個人生命記憶在廣泛事件之語意標籤自動化示範。 / In the digital era, labeling and retrieving are ways to understand the meaning behind a huge amount of lifetime archive. Foucusing on tedious and periodic general events, this study will discuss two issues: (1) the semantics of episodic memory (2) the trade-off between common and personal knowledge. Using the automatic image-tagging technique of lifelong digital archiving system, we propose the Coutinuous Reconceptualization Model which models the cognitive processing of examplar categorization based on temporal-spatial information. Integrating the common knowlegde, current personal life and hobby, the Continuous Reconceptualization Model improves the tagging efficiency. In this experiment, we compare the accuracy of cognitive modeling and tagging efficiency of the three distinct models: the common knowledge model, personal knowledge model and Coutinuous Reconceptualization Model. Nine participants were recruited to label the photos. The results show that the Continous Reconceptualization Model overcomes the limitations inherent in other models, including the auto-tagging problems of modeling certain situations, such as re-visiting places, seasonal activities, noncontinuous activities and information boundary. Consequently, the Continuous Reconceptualization Model demonstrated the efficiency of the automatic image-tagging technique used in the semantic labeling of the general event of personal memory.