17,840 research outputs found
Query generation from multiple media examples
This paper exploits an unified media document representation called feature terms for query generation from multiple media examples, e.g. images. A feature term refers to a value interval of a media feature. A media document is therefore represented by a frequency vector about feature term appearance. This approach (1) facilitates feature accumulation from multiple examples; (2) enables the exploration of text-based retrieval models for multimedia retrieval. Three statistical criteria, minimised chi-squared, minimised AC/DC rate and maximised entropy, are proposed to extract feature terms from a given media document collection. Two textual ranking functions, KL divergence and a BM25-like retrieval model, are adapted to estimate media document relevance. Experiments on the Corel photo collection and the TRECVid 2006 collection show the effectiveness of feature term based query in image and video retrieval
Bayes Merging of Multiple Vocabularies for Scalable Image Retrieval
The Bag-of-Words (BoW) representation is well applied to recent
state-of-the-art image retrieval works. Typically, multiple vocabularies are
generated to correct quantization artifacts and improve recall. However, this
routine is corrupted by vocabulary correlation, i.e., overlapping among
different vocabularies. Vocabulary correlation leads to an over-counting of the
indexed features in the overlapped area, or the intersection set, thus
compromising the retrieval accuracy. In order to address the correlation
problem while preserve the benefit of high recall, this paper proposes a Bayes
merging approach to down-weight the indexed features in the intersection set.
Through explicitly modeling the correlation problem in a probabilistic view, a
joint similarity on both image- and feature-level is estimated for the indexed
features in the intersection set.
We evaluate our method through extensive experiments on three benchmark
datasets. Albeit simple, Bayes merging can be well applied in various merging
tasks, and consistently improves the baselines on multi-vocabulary merging.
Moreover, Bayes merging is efficient in terms of both time and memory cost, and
yields competitive performance compared with the state-of-the-art methods.Comment: 8 pages, 7 figures, 6 tables, accepted to CVPR 201
Measuring concept similarities in multimedia ontologies: analysis and evaluations
The recent development of large-scale multimedia concept ontologies has provided a new momentum for research in the semantic analysis of multimedia repositories. Different methods for generic concept detection have been extensively studied, but the question of how to exploit the structure of a multimedia ontology and existing inter-concept relations has not received similar attention. In this paper, we present a clustering-based method for modeling semantic concepts on low-level feature spaces and study the evaluation of the quality of such models with entropy-based methods. We cover a variety of methods for assessing the similarity of different concepts in a multimedia ontology. We study three ontologies and apply the proposed techniques in experiments involving the visual and semantic similarities, manual annotation of video, and concept detection. The results show that modeling inter-concept relations can provide a promising resource for many different application areas in semantic multimedia processing
Topic Models Conditioned on Arbitrary Features with Dirichlet-multinomial Regression
Although fully generative models have been successfully used to model the
contents of text documents, they are often awkward to apply to combinations of
text data and document metadata. In this paper we propose a
Dirichlet-multinomial regression (DMR) topic model that includes a log-linear
prior on document-topic distributions that is a function of observed features
of the document, such as author, publication venue, references, and dates. We
show that by selecting appropriate features, DMR topic models can meet or
exceed the performance of several previously published topic models designed
for specific data.Comment: Appears in Proceedings of the Twenty-Fourth Conference on Uncertainty
in Artificial Intelligence (UAI2008
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