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Heterogeneous visual features integration for image recognition optimization in internet of things
یکپارچه سازی ویژگی های یکپارچه برای بهینه سازی تصویر در اینترنت از اشیا-2016 Recently, a large number of physical devices, together with distributed information systems, deployed in
internet of things (IoT), are collecting more and more images. Such collected images recognition poses an
important challenge on optimization in internet of things. Specially, most of existing methods only adopt
shallow learning models to integrate various features of images for recognition limiting classification
accuracy. In this paper, we propose a multimodal deep learning (MMDL) approach to integrate hetero
geneous visual features by considering each type of visual feature as one modality for image recognition
optimization in internet of things. In our scheme, we extract the high-level abstraction of each modality
by a stacked autoencoders. Furthermore, we design a back propagation algorithm with shared weights
learned from a softmax layer to update the pretrained parameters of multiple stacked autoencoders
simultaneously. The integration is performed by concatenating the last hidden layers of the multimodal
stacked autoencoders architecture. Extensive experiments are carried out on three datasets i.e. Ani
mal with Attributes, NUS-WIDE-OBJECT, and Handwritten Numerals, by comparison with SVM, SAE, and
AMMSS. Results demonstrate that our scheme has superior performance on heterogeneous visual features
integration for image recognition optimization in internet of things.
Keywords: Multimodal integration optimization | Deep learning | Internet of things | Image classification | Stacked autoencoders |
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