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BLSTM Recurrent Neural Network for Object Recognition

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DOI: 10.23977/jaip.2016.11005 | Downloads: 55 | Views: 2858

Author(s)

Yalan Qin 1

Affiliation(s)

1 College of Computer and Information Science, Southwest University, Chongqing, 400715, China

Corresponding Author

Yalan Qin

ABSTRACT

Multi-object relationship information can help eliminate some incorrect combinations or locations of objects. Moreover, it is favorable to extract scene information for object recognition. In this paper, we introduce a new way to generate image representation and propose a deep learning framework to fuse the contextual dependencies among objects and scene information in an image. It adopts a bidirectional long short-term memory recurrent neural network (BLSTM-RNN) to deal with the problem of variable-length sequence produced by local detectors in different images. Then it is applied to the existing tree context model for further recognition. Experimental results on SUN09 dataset show that our model outperforms the state-of the-art object localization methods.

KEYWORDS

Multi-object Relationship; Object Recognition; BLSTM

CITE THIS PAPER

Yalan Q. (2016) BLSTM Recurrent Neural Network for Object Recognition. Journal of Artificial Intelligence Practice (2016) 1: 25-29.

REFERENCES

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