Adaptive Energy Selection for Content-Aware Image Resizing
| Autor: | Kazuma Sasaki, Satoshi Iizuka, Edgar Simo-Serra, Zheng Ze, Yoshihiko Mochizuki, Hiroshi Ishikawa, Yuya Nagahama |
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| Rok vydání: | 2017 |
| Předmět: |
Pixel
Computer science business.industry 05 social sciences Feature extraction ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Image processing Pattern recognition 02 engineering and technology Image (mathematics) Support vector machine Seam carving Feature (computer vision) 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing 0501 psychology and cognitive sciences Artificial intelligence business 050107 human factors Energy (signal processing) |
| Zdroj: | ACPR |
| DOI: | 10.1109/acpr.2017.105 |
| Popis: | Content-aware image resizing aims to reduce the size of an image without touching important objects and regions. In seam carving, this is done by assessing the importance of each pixel by an energy function and repeatedly removing a string of pixels avoiding pixels with high energy. However, there is no single energy function that is best for all images: the optimal energy function is itself a function of the image. In this paper, we present a method for predicting the quality of the results of resizing an image with different energy functions, so as to select the energy best suited for that particular image. We formulate the selection as a classification problem; i.e., we 'classify' the input into the class of images for which one of the energies works best. The standard approach would be to use a CNN for the classification. However, the existence of a fully connected layer forces us to resize the input to a fixed size, which obliterates useful information, especially lower-level features that more closely relate to the energies used for seam carving. Instead, we extract a feature from internal convolutional layers, which results in a fixed-length vector regardless of the input size, making it amenable to classification with a Support Vector Machine. This formulation of the algorithm selection as a classification problem can be used whenever there are multiple approaches for a specific image processing task. We validate our approach with a user study, where our method outperforms recent seam carving approaches. |
| Databáze: | OpenAIRE |
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