machine-learning   16139

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[1403.6566] Image Retargeting by Content-Aware Synthesis
Real-world images usually contain vivid contents and rich textural details, which will complicate the manipulation on them. In this paper, we design a new framework based on content-aware synthesis to enhance content-aware image retargeting. By detecting the textural regions in an image, the textural image content can be synthesized rather than simply distorted or cropped. This method enables the manipulation of textural & non-textural regions with different strategy since they have different natures. We propose to retarget the textural regions by content-aware synthesis and non-textural regions by fast multi-operators. To achieve practical retargeting applications for general images, we develop an automatic and fast texture detection method that can detect multiple disjoint textural regions. We adjust the saliency of the image according to the features of the textural regions. To validate the proposed method, comparisons with state-of-the-art image targeting techniques and a user study were conducted. Convincing visual results are shown to demonstrate the effectiveness of the proposed method.
generative-art  generative-models  image-processing  machine-learning  nudge-targets  consider:performance-measures  consider:looking-to-see 
yesterday by Vaguery
[1308.0419] Inverse Procedural Modeling of Facade Layouts
In this paper, we address the following research problem: How can we generate a meaningful split grammar that explains a given facade layout? To evaluate if a grammar is meaningful, we propose a cost function based on the description length and minimize this cost using an approximate dynamic programming framework. Our evaluation indicates that our framework extracts meaningful split grammars that are competitive with those of expert users, while some users and all competing automatic solutions are less successful.
grammar  L-systems  generative-models  image-processing  learning-from-data  machine-learning  inverse-problems  nudge-targets  consider:representation  consider:feature-discovery 
yesterday by Vaguery
Hacker's guide to Neural Networks
Hacker's guide to Neural Networks - Added March 02, 2015 at 09:53AM
machine-learning  neural-networks  read2of 
yesterday by xenocid
Hello World - Machine Learning Recipes #1 - YouTube
A video series to get you started with machine learning concepts and code.
yesterday by mergesort

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