7,562 research outputs found

    Automatic tagging and geotagging in video collections and communities

    Get PDF
    Automatically generated tags and geotags hold great promise to improve access to video collections and online communi- ties. We overview three tasks offered in the MediaEval 2010 benchmarking initiative, for each, describing its use scenario, definition and the data set released. For each task, a reference algorithm is presented that was used within MediaEval 2010 and comments are included on lessons learned. The Tagging Task, Professional involves automatically matching episodes in a collection of Dutch television with subject labels drawn from the keyword thesaurus used by the archive staff. The Tagging Task, Wild Wild Web involves automatically predicting the tags that are assigned by users to their online videos. Finally, the Placing Task requires automatically assigning geo-coordinates to videos. The specification of each task admits the use of the full range of available information including user-generated metadata, speech recognition transcripts, audio, and visual features

    Digital Image Access & Retrieval

    Get PDF
    The 33th Annual Clinic on Library Applications of Data Processing, held at the University of Illinois at Urbana-Champaign in March of 1996, addressed the theme of "Digital Image Access & Retrieval." The papers from this conference cover a wide range of topics concerning digital imaging technology for visual resource collections. Papers covered three general areas: (1) systems, planning, and implementation; (2) automatic and semi-automatic indexing; and (3) preservation with the bulk of the conference focusing on indexing and retrieval.published or submitted for publicatio

    Unified Concept-based Multimedia Information Retrieval Technique

    Get PDF
    The explosion of digital data in the last two decades followed by the development of various types of data, including text, images, audio and video known as multimedia data. Multimedia Information Retrieval is required to search various type of media. There is comprehensive information need that can not be handled by the monolithic search engine like Google, Google Image, Youtube, or FindSounds. The shortcoming of search engine today related to their format or media is the dominance of text format, while the expected information could be an image, audio or video. Hence it is necessary to present multimedia format at the same time. This paper tries to design Unified Concept-based Multimedia Information Retrieval (UCpBMIR) technique to tackle those difficulties by using unified multimedia indexing. The indexing technique transforms the various of media with their features into text representation with the concept-based algorithm and put it into the concept detector. Learning model configures the concept detector to classify the multimedia object. The result of the concept detector process is placed in unified multimedia index database and waiting for the concept-based query to be matched into the Semantic Similarities with ontology. The ontology will provide the relationship between object representation of multimedia data. Due to indexing text, image, audio, and video respectively that naturally, they are heterogeneous, but conceptually they may have the relationship among them. From the preliminary result that multimedia document retrieved can be obtained through single query any format in order to retrieve all kind of multimedia format. Unified multimedia indexing technique with ontology will unify each format of multimedi

    Semantic Sound Similarity with Deep Embeddings for Freesound

    Get PDF
    Freesound is an online platform where people using sounds for various purposes can share or download audio clips. In such platforms, it is crucial that the users are provided with accurate sound recommendations, which becomes challenging due to the large size of the audio collection, complexity of the sound properties, and the human aspect of the recommendations. To provide sound recommendations, Freesound features a "similar sounds" function. However, this function primarily relies on creating a digital representation of audio clips that assesses the acoustic characteristics of sounds, which proves to be insufficient for accurately capturing their semantic properties. This limitation reduces the content-based retrieval capa-bilities of Freesound users. Moreover, the audio representation is created by hand-picking features that were engineered using domain knowledge. Today, in various fields related to audio, this approach has been replaced by using neural networks as feature extractors. In this work, we search for pretrained general-purpose neural net-works that can be used to represent the semantic content of audio clips. We choose 8 such models and compare their semantic sound similarity performances both ob-jectively and subjectively. During the integration of deep embeddings in the sound similarity system, we explore numerous design choices and share valuable insights. We use the FSD50K evaluation set for all experiments and report various objective metrics using the sound class hierarchy to perform multi-level analysis, including class- and family-level. We find out that most of the neural networks outperform the hand-made representation subjectively and objectively. Specifically, the multi-modal representation learning model CLAP that uses natural language and audio as modalities outperforms other models by a significant margin, while the models that attempt to leverage the CLIP model for creating tri-modal representations fail

    CHORUS Deliverable 2.1: State of the Art on Multimedia Search Engines

    Get PDF
    Based on the information provided by European projects and national initiatives related to multimedia search as well as domains experts that participated in the CHORUS Think-thanks and workshops, this document reports on the state of the art related to multimedia content search from, a technical, and socio-economic perspective. The technical perspective includes an up to date view on content based indexing and retrieval technologies, multimedia search in the context of mobile devices and peer-to-peer networks, and an overview of current evaluation and benchmark inititiatives to measure the performance of multimedia search engines. From a socio-economic perspective we inventorize the impact and legal consequences of these technical advances and point out future directions of research

    Music information retrieval: conceptuel framework, annotation and user behaviour

    Get PDF
    Understanding music is a process both based on and influenced by the knowledge and experience of the listener. Although content-based music retrieval has been given increasing attention in recent years, much of the research still focuses on bottom-up retrieval techniques. In order to make a music information retrieval system appealing and useful to the user, more effort should be spent on constructing systems that both operate directly on the encoding of the physical energy of music and are flexible with respect to users’ experiences. This thesis is based on a user-centred approach, taking into account the mutual relationship between music as an acoustic phenomenon and as an expressive phenomenon. The issues it addresses are: the lack of a conceptual framework, the shortage of annotated musical audio databases, the lack of understanding of the behaviour of system users and shortage of user-dependent knowledge with respect to high-level features of music. In the theoretical part of this thesis, a conceptual framework for content-based music information retrieval is defined. The proposed conceptual framework - the first of its kind - is conceived as a coordinating structure between the automatic description of low-level music content, and the description of high-level content by the system users. A general framework for the manual annotation of musical audio is outlined as well. A new methodology for the manual annotation of musical audio is introduced and tested in case studies. The results from these studies show that manually annotated music files can be of great help in the development of accurate analysis tools for music information retrieval. Empirical investigation is the foundation on which the aforementioned theoretical framework is built. Two elaborate studies involving different experimental issues are presented. In the first study, elements of signification related to spontaneous user behaviour are clarified. In the second study, a global profile of music information retrieval system users is given and their description of high-level content is discussed. This study has uncovered relationships between the users’ demographical background and their perception of expressive and structural features of music. Such a multi-level approach is exceptional as it included a large sample of the population of real users of interactive music systems. Tests have shown that the findings of this study are representative of the targeted population. Finally, the multi-purpose material provided by the theoretical background and the results from empirical investigations are put into practice in three music information retrieval applications: a prototype of a user interface based on a taxonomy, an annotated database of experimental findings and a prototype semantic user recommender system. Results are presented and discussed for all methods used. They show that, if reliably generated, the use of knowledge on users can significantly improve the quality of music content analysis. This thesis demonstrates that an informed knowledge of human approaches to music information retrieval provides valuable insights, which may be of particular assistance in the development of user-friendly, content-based access to digital music collections

    The COST292 experimental framework for TRECVID 2007

    Get PDF
    In this paper, we give an overview of the four tasks submitted to TRECVID 2007 by COST292. In shot boundary (SB) detection task, four SB detectors have been developed and the results are merged using two merging algorithms. The framework developed for the high-level feature extraction task comprises four systems. The first system transforms a set of low-level descriptors into the semantic space using Latent Semantic Analysis and utilises neural networks for feature detection. The second system uses a Bayesian classifier trained with a “bag of subregions”. The third system uses a multi-modal classifier based on SVMs and several descriptors. The fourth system uses two image classifiers based on ant colony optimisation and particle swarm optimisation respectively. The system submitted to the search task is an interactive retrieval application combining retrieval functionalities in various modalities with a user interface supporting automatic and interactive search over all queries submitted. Finally, the rushes task submission is based on a video summarisation and browsing system comprising two different interest curve algorithms and three features

    Music Synchronization, Audio Matching, Pattern Detection, and User Interfaces for a Digital Music Library System

    Get PDF
    Over the last two decades, growing efforts to digitize our cultural heritage could be observed. Most of these digitization initiatives pursuit either one or both of the following goals: to conserve the documents - especially those threatened by decay - and to provide remote access on a grand scale. For music documents these trends are observable as well, and by now several digital music libraries are in existence. An important characteristic of these music libraries is an inherent multimodality resulting from the large variety of available digital music representations, such as scanned score, symbolic score, audio recordings, and videos. In addition, for each piece of music there exists not only one document of each type, but many. Considering and exploiting this multimodality and multiplicity, the DFG-funded digital library initiative PROBADO MUSIC aimed at developing a novel user-friendly interface for content-based retrieval, document access, navigation, and browsing in large music collections. The implementation of such a front end requires the multimodal linking and indexing of the music documents during preprocessing. As the considered music collections can be very large, the automated or at least semi-automated calculation of these structures would be recommendable. The field of music information retrieval (MIR) is particularly concerned with the development of suitable procedures, and it was the goal of PROBADO MUSIC to include existing and newly developed MIR techniques to realize the envisioned digital music library system. In this context, the present thesis discusses the following three MIR tasks: music synchronization, audio matching, and pattern detection. We are going to identify particular issues in these fields and provide algorithmic solutions as well as prototypical implementations. In Music synchronization, for each position in one representation of a piece of music the corresponding position in another representation is calculated. This thesis focuses on the task of aligning scanned score pages of orchestral music with audio recordings. Here, a previously unconsidered piece of information is the textual specification of transposing instruments provided in the score. Our evaluations show that the neglect of such information can result in a measurable loss of synchronization accuracy. Therefore, we propose an OCR-based approach for detecting and interpreting the transposition information in orchestral scores. For a given audio snippet, audio matching methods automatically calculate all musically similar excerpts within a collection of audio recordings. In this context, subsequence dynamic time warping (SSDTW) is a well-established approach as it allows for local and global tempo variations between the query and the retrieved matches. Moving to real-life digital music libraries with larger audio collections, however, the quadratic runtime of SSDTW results in untenable response times. To improve on the response time, this thesis introduces a novel index-based approach to SSDTW-based audio matching. We combine the idea of inverted file lists introduced by Kurth and MĂŒller (Efficient index-based audio matching, 2008) with the shingling techniques often used in the audio identification scenario. In pattern detection, all repeating patterns within one piece of music are determined. Usually, pattern detection operates on symbolic score documents and is often used in the context of computer-aided motivic analysis. Envisioned as a new feature of the PROBADO MUSIC system, this thesis proposes a string-based approach to pattern detection and a novel interactive front end for result visualization and analysis
    • 

    corecore