摘要 :
Because of the media digitization, a large amount of infor- mation such as speech, audio and video data is produced everyday. In order to retrieve data for these databases quickly and precisely, multimedia tech- nologies for struc...
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Because of the media digitization, a large amount of infor- mation such as speech, audio and video data is produced everyday. In order to retrieve data for these databases quickly and precisely, multimedia tech- nologies for structuring and retrieving of speech, audio and video data are strongly required. In this paper, we overview the multimedia technologies such as structuring and retrieval of speech, audio an video data, speaker indexing, audio summarization and cross media retrieval existing today for RV news detabase.
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This paper aims to bridge human hearing and vision from the viewpoint of database search for images or music. The semantic content of an image can be illustrated with music or conversely images can be associated with a piece of mu...
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This paper aims to bridge human hearing and vision from the viewpoint of database search for images or music. The semantic content of an image can be illustrated with music or conversely images can be associated with a piece of music. The theoretical basis of the bridge is synaesthesia, a property of human perception. A prototype cross-media retrieval system is built, using principles established in the neuroscientific study of synaesthesia.
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摘要 :
Collecting and organizing is a basic human activity which leads to the need for retrieval. The author explains what this means for media searching and presents a proposition for what research on media searching should address.
摘要 :
Purpose - The purpose of this paper is to elaborate how source preference criteria are defined in the
context of everyday projects that require the seeking of problem-specific information. More
specifically, to find out how info...
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Purpose - The purpose of this paper is to elaborate how source preference criteria are defined in the
context of everyday projects that require the seeking of problem-specific information. More
specifically, to find out how information seekers explain their preference criteria by characterizing the
perceived strengths and weaknesses of diverse sources.
Design/methodology/approach - The approach takes the form of qualitative content analysis of
empirical data gathered by semi-structured interviews with 16 prospective home buyers in 2008. The
source preference criteria were elicited by making use of the construct of information source horizon.
Findings - Networked sources were favoured most strongly, followed by printed media, human
sources and organizational sources. Content of information was the primary source preference
criterion. Availability of information was a fairly important criterion, while user characteristics,
usability of information and situational factors were fairly marginal in this regard. In the definition of
the preference criteria, more emphasis was placed on the perceived strengths than weaknesses of
sources. Positive qualities such as "provides updated information" were referred to particulariy while
judging the relevance of the networked sources. Negative qualities like "outdated information" were
primarily associated with printed media and organizational sources.
Research limitations/implications - The study is exploratory, drawing on a relatively small
sample recruited through a web-based service. Thus, the findings cannot be generalized to prospective
home buyers.
Practical implications - Prospective home buyers tend to favour web-based information sources
and services. They should provide the customers with detailed information about the property,
including photos.
Originality/value - The paper specifies the picture of user-defined relevance judgment in the
context of everyday life information seeking.
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In this paper we tackle the problem of image search when the query is a short textual description of the image the user is looking for. We choose to implement the actual search process as a similarity search in a visual feature sp...
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In this paper we tackle the problem of image search when the query is a short textual description of the image the user is looking for. We choose to implement the actual search process as a similarity search in a visual feature space, by learning to translate a textual query into a visual representation. Searching in the visual feature space has the advantage that any update to the translation model does not require to reprocess the (typically huge) image collection on which the search is performed. We propose various neural network models of increasing complexity that learn to generate, from a short descriptive text, a high level visual representation in a visual feature space such as the pool5 layer of the ResNet-152 or the fc6–fc7 layers of an AlexNet trained on ILSVRC12 and Places databases. The Text2Vis models we explore include (1) a relatively simple regressor network relying on a bag-of-words representation for the textual descriptors, (2) a deep recurrent network that is sensible to word order, and (3) a wide and deep model that combines a stacked LSTM deep network with a wide regressor network. We compare the models we propose with other search strategies, also including textual search methods that exploit state-of-the-art caption generation models to index the image collection.
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This document describes the IR research group at the University of Duisburg-Essen, which works on quantitative modeis of interactive retrieval, social media analysis, multilingual argument retrieval and validity of IR experiments.
摘要 :
Twitter provides access to latest information. Whenever a major event happens, people try to search for event related information in social media platforms like Twitter. So, it is essential to develop methods to get good quality o...
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Twitter provides access to latest information. Whenever a major event happens, people try to search for event related information in social media platforms like Twitter. So, it is essential to develop methods to get good quality of event related tweets. People share different opinions, feelings, feedback, etc. about events happening around the world in Twitter in the form of tweets. These tweets are often short and contain noise. So, it is very difficult to get the most relevant data for a given event from Twitter. We propose a two-phase approach to retrieve the tweets related to planned events. In the first phase, initial retrieval is done by using BM25 algorithm. In the second phase, reranking is done by combining three scoring mechanisms namely BM25 score, top hashtags score related to an event, and top TF-IDF terms score related to an event. A learning to rank algorithm SVM_Rank is applied to give weights to these three methods and combine them to get the final score of the tweet. We performed experiments on two benchmark datasets CLEF and TREC. Experimental results show that our method outperforms baseline and literature methods for both the datasets according to multiple evaluation metrics.
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摘要 :
A large amount of social media hosted on platforms like Flickr and Instagram is related to social events. The task of social event classification refers to the distinction of event and non-event-related contents as well as the cla...
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A large amount of social media hosted on platforms like Flickr and Instagram is related to social events. The task of social event classification refers to the distinction of event and non-event-related contents as well as the classification of event types (e.g. sports events and concerts). In this paper, we provide an extensive study of textual, visual, as well as multimodal representations for social event classification. We investigate the strengths and weaknesses of the modalities and study the synergy effects between the modalities. Experimental results obtained with our multimodal representation outperform state-of-the-art methods and provide a new baseline for future research. (C) 2016 Elsevier B.V. All rights reserved.
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摘要 :
Purpose - The purpose of this paper is to describe a new ontological content-based filtering method for ranking the relevance of items for readers of news items, and its evaluation. The method has been implemented in ePaper, a per...
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Purpose - The purpose of this paper is to describe a new ontological content-based filtering method for ranking the relevance of items for readers of news items, and its evaluation. The method has been implemented in ePaper, a personalised electronic newspaper prototype system. The method utilises a hierarchical ontology of news; it considers common and related concepts appearing in a user's profile on the one hand, and in a news item's profile on the other hand, and measures the "hierarchical distances" between these concepts. On that basis it computes the similarity between item and user profiles and rank-orders the news items according to their relevance to each user. Design/methodology/approach - The paper evaluates the performance of the filtering method in an experimental setting. Each participant read news items obtained from an electronic newspaper and rated their relevance. Independently, the filtering method is applied to the same items and generated, for each participant, a list of news items ranked according to relevance. Findings - The results of the evaluations revealed that the filtering algorithm, which takes into consideration hierarchically related concepts, yielded significantly better results than a filtering method that takes only common concepts into consideration. The paper determined a best set of values (weights) of the hierarchical similarity parameters. It also found out that the quality of filtering improves as the number of items used for implicit updates of the profile increases, and that even with implicitly updated profiles, it is better to start with user-defined profiles. Originality/value - The proposed content-based filtering method can be used for filtering not only news items but items from any domain, and not only with a three-level hierarchical ontology but any-level ontology, in any language.
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Purpose - The purpose of the paper is to analyse the structure of a small number of abstracts that have appeared in the CABI database over a number of years, during which time the authorship of the abstracts changed from CABI edit...
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Purpose - The purpose of the paper is to analyse the structure of a small number of abstracts that have appeared in the CABI database over a number of years, during which time the authorship of the abstracts changed from CABI editorial staff to journal article authors themselves. This paper reports a study of the semantic organisation and thematic structure of 12 abstracts from the field of protozoology in an effort to discover whether these abstracts followed generally agreed abstracting guidelines.
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