کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
526768 869224 2015 12 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Predicting memorability of images using attention-driven spatial pooling and image semantics
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر چشم انداز کامپیوتر و تشخیص الگو
پیش نمایش صفحه اول مقاله
Predicting memorability of images using attention-driven spatial pooling and image semantics
چکیده انگلیسی


• We examine the role of visual attention and image semantics in understanding image memorability.
• We propose an attention-driven spatial pooling strategy for image memorability.
• Considering image features from the salient parts of images improves the results of the previous models.
• We also investigate different semantic properties of images.
• Combining attention-driven pooling with semantic features yields state-of-the-art results.

In daily life, humans demonstrate an amazing ability to remember images they see on magazines, commercials, TV, web pages, etc. but automatic prediction of intrinsic memorability of images using computer vision and machine learning techniques has only been investigated very recently. Our goal in this article is to explore the role of visual attention and image semantics in understanding image memorability. In particular, we present an attention-driven spatial pooling strategy and show that considering image features from the salient parts of images improves the results of the previous models. We also investigate different semantic properties of images by carrying out an analysis of a diverse set of recently proposed semantic features which encode meta-level object categories, scene attributes, and invoked feelings. We show that these features which are automatically extracted from images provide memorability predictions as nearly accurate as those derived from human annotations. Moreover, our combined model yields results superior to those of state-of-the art fully automatic models.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Image and Vision Computing - Volume 42, October 2015, Pages 35–46
نویسندگان
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