IEEE Trans Image Process - Fast semantic diffusion for large-scale context-based image and video annotation.

Tópicos

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Resumo

Exploring context information for visual recognition has recently received significant research attention. This paper proposes a novel and highly efficient approach, which is named semantic diffusion, to utilize semantic context for large-scale image and video annotation. Starting from the initial annotation of a large number of semantic concepts (categories), obtained by either machine learning or manual tagging, the proposed approach refines the results using a graph diffusion technique, which recovers the consistency and smoothness of the annotations over a semantic graph. Different from the existing graph-based learning methods that model relations among data samples, the semantic graph captures context by treating the concepts as nodes and the concept affinities as the weights of edges. In particular, our approach is capable of simultaneously improving annotation accuracy and adapting the concept affinities to new test data. The adaptation provides a means to handle domain change between training and test data, which often occurs in practice. Extensive experiments are conducted to improve concept annotation results using Flickr images and TV program videos. Results show consistent and significant performance gain (10 +% on both image and video data sets). Source codes of the proposed algorithms are available online.

Resumo Limpo

explor context inform visual recognit recent receiv signific research attent paper propos novel high effici approach name semant diffus util semant context largescal imag video annot start initi annot larg number semant concept categori obtain either machin learn manual tag propos approach refin result use graph diffus techniqu recov consist smooth annot semant graph differ exist graphbas learn method model relat among data sampl semant graph captur context treat concept node concept affin weight edg particular approach capabl simultan improv annot accuraci adapt concept affin new test data adapt provid mean handl domain chang train test data often occur practic extens experi conduct improv concept annot result use flickr imag tv program video result show consist signific perform gain imag video data set sourc code propos algorithm avail onlin

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