DANIEL: A Deep Architecture for Automatic Analysis and Retrieval of Building Floor Plans
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Additional excerpts
...By emerging Convolutional Neural Networks (CNN), In [38] a deep CNN is presented for feature extraction to address the limitation of conventional image processing techniques for extracting features....
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4 citations
4 citations
4 citations
Cites background or methods from "DANIEL: A Deep Architecture for Aut..."
...They are: (i) Systems Evaluation SYnthetic Documents (SESYD) [30] (ii) Computer Vision Center Floor Plan (CVC-FP) [31] and (iii) Repository Of BuildIng plaNs (ROBIN) [29]....
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...Figure 6 shows the 12 decor symbols used in the dataset [29]....
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3 citations
Cites background from "DANIEL: A Deep Architecture for Aut..."
...One of the most relevant recently developed approaches uses deep learning to extract semantic features from floor plan images (Sharma et al. 2017)....
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References
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"DANIEL: A Deep Architecture for Aut..." refers methods in this paper
...We have shown the P-R plot for only proposed ROBIN Dataset, as for SESYD dataset our proposed and some of the existing state-of-the-art techniques [2], [3], [15] yielded flat PR curve (Precision value 1 for all Recall values)....
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...Hence, when the same features (HOG, SIFT, RLH) are used under a canonical CBIR paradigm, they yield superior results than OASIS (see Tab....
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...Several generic image retrieval systems were also proposed in the past [13], [14], where features like Histogram of Oriented Gradients (HOG) [15], Bag of Features (BOF) [16], Local Binary Pattern (LBP) [17] and Run-Length Histogram [18] has been used....
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12,449 citations
"DANIEL: A Deep Architecture for Aut..." refers background in this paper
...Rotation and translation invariant features for symbol spotting in documents were proposed in [22], and [23]....
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10,161 citations
"DANIEL: A Deep Architecture for Aut..." refers background in this paper
...layers is switched (pooling is done before normalization) [28]....
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