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1 Vision-language integration using constrained local semantic features
ادغام بینش زبان با استفاده از ویژگی های معنایی محلی محدود-2017
Article history:Received 14 September 2016Revised 5 May 2017Accepted 30 May 2017 Available online xxxKeywords:Image classification Image retrievalBi-modal classification Semantic featuresConcept-based sparsification Constrained local regions Vision-language integration Common latent spacePure concept spaceThis paper tackles two recent promising issues in the field of computer vision, namely “the integration of linguistic and visual information” and “the use of semantic features to represent the image content”. Semantic features represent images according to some visual concepts that are detected into the image by a set of base classifiers. Recent works exhibit competitive performances in image classification and retrieval using such features. We propose to rely on this type of image descriptions to facilitate its inte- gration with linguistic data. More precisely, the contribution of this paper is threefold. First, we propose to automatically determine the most useful dimensions of a semantic representation according to the actual image content. Hence, it results into a level of sparsity for the semantic features that is adapted to each image independently. Our model takes into account both the confidence on each base classifier and the global amount of information of the semantic signature, defined in the Shannon sense. This con- tribution is further extended to better reflect the detection of a visual concept at a local scale. Second, we introduce a new strategy to learn an efficient mid-level representation by CNNs that boosts the per- formance of semantic signatures. Last, we propose several schemes to integrate a visual representation based on semantic features with some linguistic piece of information, leading to the nesting of linguistic information at two levels of the visual features. Experimental validation is conducted on four bench- marks (VOC 2007, VOC 2012, Nus-Wide and MIT Indoor) for classification, three of them for retrieval and two of them for bi-modal classification. The proposed semantic feature achieves state-of-the-art perfor- mances on three classification benchmarks and all retrieval ones. Regarding our vision-language integra- tion method, it achieves state-of-the-art performances in bi-modal classification.© 2017 Elsevier Inc. All rights reserved.
Keywords: Image classification | Image retrieval Bi-modal classification | Semantic features | Concept-based sparsification | Constrained local regions | Vision-language integration | Common latent space | Pure concept space
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