12,825 research outputs found
Binary Patterns Encoded Convolutional Neural Networks for Texture Recognition and Remote Sensing Scene Classification
Designing discriminative powerful texture features robust to realistic
imaging conditions is a challenging computer vision problem with many
applications, including material recognition and analysis of satellite or
aerial imagery. In the past, most texture description approaches were based on
dense orderless statistical distribution of local features. However, most
recent approaches to texture recognition and remote sensing scene
classification are based on Convolutional Neural Networks (CNNs). The d facto
practice when learning these CNN models is to use RGB patches as input with
training performed on large amounts of labeled data (ImageNet). In this paper,
we show that Binary Patterns encoded CNN models, codenamed TEX-Nets, trained
using mapped coded images with explicit texture information provide
complementary information to the standard RGB deep models. Additionally, two
deep architectures, namely early and late fusion, are investigated to combine
the texture and color information. To the best of our knowledge, we are the
first to investigate Binary Patterns encoded CNNs and different deep network
fusion architectures for texture recognition and remote sensing scene
classification. We perform comprehensive experiments on four texture
recognition datasets and four remote sensing scene classification benchmarks:
UC-Merced with 21 scene categories, WHU-RS19 with 19 scene classes, RSSCN7 with
7 categories and the recently introduced large scale aerial image dataset (AID)
with 30 aerial scene types. We demonstrate that TEX-Nets provide complementary
information to standard RGB deep model of the same network architecture. Our
late fusion TEX-Net architecture always improves the overall performance
compared to the standard RGB network on both recognition problems. Our final
combination outperforms the state-of-the-art without employing fine-tuning or
ensemble of RGB network architectures.Comment: To appear in ISPRS Journal of Photogrammetry and Remote Sensin
User Constrained Thumbnail Generation using Adaptive Convolutions
Thumbnails are widely used all over the world as a preview for digital
images. In this work we propose a deep neural framework to generate thumbnails
of any size and aspect ratio, even for unseen values during training, with high
accuracy and precision. We use Global Context Aggregation (GCA) and a modified
Region Proposal Network (RPN) with adaptive convolutions to generate thumbnails
in real time. GCA is used to selectively attend and aggregate the global
context information from the entire image while the RPN is used to predict
candidate bounding boxes for the thumbnail image. Adaptive convolution
eliminates the problem of generating thumbnails of various aspect ratios by
using filter weights dynamically generated from the aspect ratio information.
The experimental results indicate the superior performance of the proposed
model over existing state-of-the-art techniques.Comment: International Conference on Acoustics, Speech, and Signal
Processing(ICASSP), 201
Speeding up Convolutional Neural Networks with Low Rank Expansions
The focus of this paper is speeding up the evaluation of convolutional neural
networks. While delivering impressive results across a range of computer vision
and machine learning tasks, these networks are computationally demanding,
limiting their deployability. Convolutional layers generally consume the bulk
of the processing time, and so in this work we present two simple schemes for
drastically speeding up these layers. This is achieved by exploiting
cross-channel or filter redundancy to construct a low rank basis of filters
that are rank-1 in the spatial domain. Our methods are architecture agnostic,
and can be easily applied to existing CPU and GPU convolutional frameworks for
tuneable speedup performance. We demonstrate this with a real world network
designed for scene text character recognition, showing a possible 2.5x speedup
with no loss in accuracy, and 4.5x speedup with less than 1% drop in accuracy,
still achieving state-of-the-art on standard benchmarks
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