Automatic Synchronization of Multi-User Photo Galleries

In this paper we address the issue of photo galleries synchronization, where pictures related to the same event are collected by different users. Existing solutions to address the problem are usually based on unrealistic assumptions, like time consistency across photo galleries, and often heavily rely on heuristics, limiting therefore the applicability to real-world scenarios. We propose a solution that achieves better generalization performance for the synchronization task compared to the available literature. The method is characterized by three stages: at first, deep convolutional neural network features are used to assess the visual similarity among the photos; then, pairs of similar photos are detected across different galleries and used to construct a graph; eventually, a probabilistic graphical model is used to estimate the temporal offset of each pair of galleries, by traversing the minimum spanning tree extracted from this graph. The experimental evaluation is conducted on four publicly available datasets covering different types of events, demonstrating the strength of our proposed method. A thorough discussion of the obtained results is provided for a critical assessment of the quality in synchronization.Comment: ACCEPTED to IEEE Transactions on Multimedi

Smartphone picture organization: a hierarchical approach

We live in a society where the large majority of the population has a camera-equipped smartphone. In addition, hard drives and cloud storage are getting cheaper and cheaper, leading to a tremendous growth in stored personal photos. Unlike photo collections captured by a digital camera, which typically are pre-processed by the user who organizes them into event-related folders, smartphone pictures are automatically stored in the cloud. As a consequence, photo collections captured by a smartphone are highly unstructured and because smartphones are ubiquitous, they present a larger variability compared to pictures captured by a digital camera. To solve the need of organizing large smartphone photo collections automatically, we propose here a new methodology for hierarchical photo organization into topics and topic-related categories. Our approach successfully estimates latent topics in the pictures by applying probabilistic Latent Semantic Analysis, and automatically assigns a name to each topic by relying on a lexical database. Topic-related categories are then estimated by using a set of topic-specific Convolutional Neuronal Networks. To validate our approach, we ensemble and make public a large dataset of more than 8,000 smartphone pictures from 40 persons. Experimental results demonstrate major user satisfaction with respect to state of the art solutions in terms of organization.Peer ReviewedPreprin

Event Based Media Indexing

Multimedia data, being multidimensional by its nature, requires appropriate approaches for its organizing and sorting. The growing number of sensors for capturing the environmental conditions in the moment of media creation enriches data with context-awareness. This unveils enormous potential for eventcentred multimedia processing paradigm. The essence of this paradigm lies in using events as the primary means for multimedia integration, indexing and management. Events have the ability to semantically encode relationships of different informational modalities. These modalities can include, but are not limited to: time, space, involved agents and objects. As a consequence, media processing based on events facilitates information perception by humans. This, in turn, decreases the individual’s effort for annotation and organization processes. Moreover events can be used for reconstruction of missing data and for information enrichment. The spatio-temporal component of events is a key to contextual analysis. A variety of techniques have recently been presented to leverage contextual information for event-based analysis in multimedia. The content-based approach has demonstrated its weakness in the field of event analysis, especially for the event detection task. However content-based media analysis is important for object detection and recognition and can therefore play a role which is complementary to that of event-driven context recognition. The main contribution of the thesis lies in the investigation of a new eventbased paradigm for multimedia integration, indexing and management. In this dissertation we propose i) a novel model for event based multimedia representation, ii) a robust approach for mining events from multimedia and iii) exploitation of detected events for data reconstruction and knowledge enrichment

Digital life stories: Semi-automatic (auto)biographies within lifelog collections

Our life stories enable us to reflect upon and share our personal histories. Through emerging digital technologies the possibility of collecting life experiences digitally is increasingly feasible; consequently so is the potential to create a digital counterpart to our personal narratives. In this work, lifelogging tools are used to collect digital artifacts continuously and passively throughout our day. These include images, documents, emails and webpages accessed; texts messages and mobile activity. This range of data when brought together is known as a lifelog. Given the complexity, volume and multimodal nature of such collections, it is clear that there are significant challenges to be addressed in order to achieve coherent and meaningful digital narratives of our events from our life histories. This work investigates the construction of personal digital narratives from lifelog collections. It examines the underlying questions, issues and challenges relating to construction of personal digital narratives from lifelogs. Fundamentally, it addresses how to organize and transform data sampled from an individual’s day-to-day activities into a coherent narrative account. This enquiry is enabled by three 20-month long-term lifelogs collected by participants and produces a narrative system which enables the semi-automatic construction of digital stories from lifelog content. Inspired by probative studies conducted into current practices of curation, from which a set of fundamental requirements are established, this solution employs a 2-dimensional spatial framework for storytelling. It delivers integrated support for the structuring of lifelog content and its distillation into storyform through information retrieval approaches. We describe and contribute flexible algorithmic approaches to achieve both. Finally, this research inquiry yields qualitative and quantitative insights into such digital narratives and their generation, composition and construction. The opportunities for such personal narrative accounts to enable recollection, reminiscence and reflection with the collection owners are established and its benefit in sharing past personal experience experiences is outlined. Finally, in a novel investigation with motivated third parties we demonstrate the opportunities such narrative accounts may have beyond the scope of the collection owner in: personal, societal and cultural explorations, artistic endeavours and as a generational heirloom

Discriminative Probabilistic Pattern Mining using Graph for Electronic Health Records

학위논문(석사)--서울대학교 대학원 :공과대학 컴퓨터공학부,2019. 8. 김선.전자의료기록(Electronic Health Records)의 임상 노트에는 환자의 병력에 대한 유용한 정보가 많이 포함되어 있다. 그러나 임상 노트는 체계화되지 않은 데이터이며 그 양은 나날이 증가하고 있다. 따라서 임상 노트를 그룹화하고 분류하기 위한 신뢰할 수 있는 데이터 마이닝 기술이 필요하다. 기존의 데이터 마이닝 기술은 키워드의 빈도를 기반으로 생성된 빈발 패턴(frequent patterns)을 이용하여 그룹 분류 작업(classification)을 수행한다. 하지만 이러한 빈발 패턴은 전자의료기록의 임상 노트와 같이 복잡한 데이터의 분류를 위해 필요한 충분히 강력하고 명확하게 구별되는 특징을 갖고 있지 않다. 또한 빈발 패턴 기반 기술은 대규모 전자의료기록 데이터에 적용될 때 확장성과 계산 비용의 문제에 직면한다. 따라서 본 연구에서는 이러한 문제점을 해결하기 위해 확률적 판별 패턴 마이닝(discriminative probabilistic pattern mining) 알고리즘을 소개한다. 확률적 판별 패턴 마이닝 알고리즘에서는 전자의료기록의 임상 노트를 분류하기 위해 그래프 구조를 도입하여 빈발 패턴의 부분 그래프를 생성하게 된다. 본 연구에서는 판별력을 높이기 위해 개별 키워드를 사용하는 대신 이진 특성 조합에서의 동시 출현(co-occurrence)을 사용하여 임상 노트 분류를 위한 빈발 패턴 그래프를 구성한다. 각각의 동시 출현은 판별력(discriminative power)에 따른 log-odds 값으로 그 가중치를 갖는다. 임상 노트의 본질을 반영하는 그래프를 찾기 위해 확률적 판별 부분 그래프 검색을 수행하며 그래프의 허브(hub) 노드에서 시작하여 동적 프로그래밍(dynamic programming)을 사용하여 경로를 찾는다. 이러한 방법으로 검색한 빈발 부분 그래프를 이용하여 전자의료기록의 임상 노트에 대한 분류 작업을 수행하게 된다.Electronic Health Records (EHR) contains plenty of useful information about patients medical history. However, EHR is highly unstructured data and amount of it is growing continuously, that is why there is a need in a reliable data mining technique to group and categorize clinical notes. Although, many existing data mining techniques for group classification use frequent patterns generated based on frequencies of keywords, these patterns do not possess strong enough distinguishing characteristics to show the difference between datasets to classify complex data such as clinical notes in EHR. Also, these techniques encounter scalability and computational cost problems when used on large EHR dataset. To address these issues, we introduce discriminative probabilistic pattern mining algorithm that uses a graph (DPPMG) to generate the subgraphs of frequent patterns for classification in electronic health records. We use co-occurrence, a combination of binary features, which is more discriminative than individual keywords to construct discriminative probabilistic frequent patterns graph for clinical notes classification. Each co-occurrence has a weight of log-odds score that is associated with its discriminative power. The graph, which reflects the essence of clinical notes is searched to find discriminative probabilistic frequent subgraphs. To discover the discriminative frequent subgraphs, we start from a hub node in the graph and use dynamic programming to find a path. The discriminative probabilistic frequent subgraphs discovered by this approach are later used to classify clinical notes of electronic health records.Chapter 1 Introduction and Motivation 1 Chapter 2 Background 4 2.1 Frequent Pattern Based Classification 4 2.2 Discriminative Pattern Mining 5 2.3 Electronic Health Records 6 Chapter 3 Related Work 8 Chapter 4 Overview and Design 10 Chapter 5 Implementation 12 5.1 Dataset 12 5.2 Keyword Extraction and Filtering 15 5.3 Co-occurrence Generation and Graph Construction 16 5.4 Dynamic Programming to Discover Optimal Path 17 Chapter 6 Results and Evaluation 20 6.1 Choosing Starting Hub Node 20 6.2 Qualitative Analysis 22 6.3 Discriminative Power of the Probabilistic Frequent Patterns 24 Chapter 7 Conclusion 26 Bibliography 28 요약 33Maste

REAL TIME ASSISTANCE IN PHOTOGRAPHY USING SOCIAL MEDIA

Ph.DDOCTOR OF PHILOSOPH