63,253 research outputs found
Audio Content-Based Music Retrieval
The rapidly growing corpus of digital audio material requires novel
retrieval strategies for exploring large music collections. Traditional retrieval strategies rely on metadata that describe the actual audio content in words. In the case that such textual descriptions are not available, one requires content-based retrieval strategies which only utilize the raw audio material. In this contribution, we discuss content-based retrieval strategies that
follow the query-by-example paradigm: given an audio query, the task is to retrieve all documents that are somehow similar or related to the query from a music collection. Such strategies can be loosely classified according to their "specificity", which refers to the degree of similarity between the query and the database documents. Here, high specificity refers to a strict notion of similarity, whereas low specificity to a rather vague one. Furthermore, we introduce a second classification principle based on "granularity", where one distinguishes between fragment-level and document-level retrieval. Using a classification scheme based on specificity and granularity, we identify various classes of retrieval scenarios, which comprise "audio identification", "audio matching", and "version
identification". For these three important classes, we give an overview of representative state-of-the-art approaches, which also illustrate the sometimes subtle but crucial differences between the retrieval scenarios. Finally, we give an outlook on a user-oriented retrieval system, which combines the various retrieval strategies in a unified framework
AXES at TRECVid 2011
The AXES project participated in the interactive known-item search task (KIS) and the interactive instance search task (INS) for TRECVid 2011. We used the same system architecture and a nearly identical user interface for both the KIS and INS tasks. Both systems made use of text search on ASR, visual concept detectors, and visual similarity search. The user experiments were carried out with media professionals and media students at the Netherlands Institute for Sound and Vision, with media professionals performing the KIS task and media students participating in the INS task. This paper describes the results and findings of our experiments
Unsupervised Visual and Textual Information Fusion in Multimedia Retrieval - A Graph-based Point of View
Multimedia collections are more than ever growing in size and diversity.
Effective multimedia retrieval systems are thus critical to access these
datasets from the end-user perspective and in a scalable way. We are interested
in repositories of image/text multimedia objects and we study multimodal
information fusion techniques in the context of content based multimedia
information retrieval. We focus on graph based methods which have proven to
provide state-of-the-art performances. We particularly examine two of such
methods : cross-media similarities and random walk based scores. From a
theoretical viewpoint, we propose a unifying graph based framework which
encompasses the two aforementioned approaches. Our proposal allows us to
highlight the core features one should consider when using a graph based
technique for the combination of visual and textual information. We compare
cross-media and random walk based results using three different real-world
datasets. From a practical standpoint, our extended empirical analysis allow us
to provide insights and guidelines about the use of graph based methods for
multimodal information fusion in content based multimedia information
retrieval.Comment: An extended version of the paper: Visual and Textual Information
Fusion in Multimedia Retrieval using Semantic Filtering and Graph based
Methods, by J. Ah-Pine, G. Csurka and S. Clinchant, submitted to ACM
Transactions on Information System
Structural Regularities in Text-based Entity Vector Spaces
Entity retrieval is the task of finding entities such as people or products
in response to a query, based solely on the textual documents they are
associated with. Recent semantic entity retrieval algorithms represent queries
and experts in finite-dimensional vector spaces, where both are constructed
from text sequences.
We investigate entity vector spaces and the degree to which they capture
structural regularities. Such vector spaces are constructed in an unsupervised
manner without explicit information about structural aspects. For concreteness,
we address these questions for a specific type of entity: experts in the
context of expert finding. We discover how clusterings of experts correspond to
committees in organizations, the ability of expert representations to encode
the co-author graph, and the degree to which they encode academic rank. We
compare latent, continuous representations created using methods based on
distributional semantics (LSI), topic models (LDA) and neural networks
(word2vec, doc2vec, SERT). Vector spaces created using neural methods, such as
doc2vec and SERT, systematically perform better at clustering than LSI, LDA and
word2vec. When it comes to encoding entity relations, SERT performs best.Comment: ICTIR2017. Proceedings of the 3rd ACM International Conference on the
Theory of Information Retrieval. 201
Human assessments of document similarity
Two studies are reported that examined the reliability of human assessments of document similarity and the association between human ratings and the results of n-gram automatic text analysis (ATA). Human interassessor reliability (IAR) was moderate to poor. However, correlations between average human ratings and n-gram solutions were strong. The average correlation between ATA and individual human solutions was greater than IAR. N-gram length influenced the strength of association, but optimum string length depended on the nature of the text (technical vs. nontechnical). We conclude that the methodology applied in previous studies may have led to overoptimistic views on human reliability, but that an optimal n-gram solution can provide a good approximation of the average human assessment of document similarity, a result that has important implications for future development of document visualization systems
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