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Locally Supervised Deep Hybrid Model for Scene Recognition.

Sheng Guo, Weilin Huang, Limin Wang

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|January 24, 2017

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This study introduces a new hybrid model that enhances convolutional features for scene recognition. The model significantly improves accuracy on benchmark datasets by combining local and global image information.

Area of Science:

Computer Vision
Machine Learning
Deep Learning

Background:

Convolutional Neural Networks (CNNs) excel at image classification using deep features.
Middle-layer convolutional features contain valuable local information often overlooked.
Existing methods struggle to fully leverage these local features for scene representation.

Purpose of the Study:

To propose a novel locally supervised deep hybrid model (LS-DHM) for enhanced scene recognition.
To effectively explore and utilize convolutional features for improved image representation.
To address the loss of local structural information in traditional deep features.

Main Methods:

Developed a new local convolutional supervision layer to propagate label information to convolutional layers.

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Introduced an efficient Fisher Convolutional Vector (FCV) to encode mid-level semantic information into a fixed-length representation.

Collaboratively employed FCVs and fully connected (FC) features within the LS-DHM framework.

Main Results:

Achieved 83.75% accuracy on the MIT Indoor67 dataset.
Obtained 67.56% accuracy on the SUN397 dataset.
Demonstrated substantial advancements over the state-of-the-art in scene recognition tasks.

Conclusions:

The LS-DHM effectively enhances and utilizes convolutional features for scene recognition.
Combining local (FCV) and global (FC-features) representations yields superior performance.
The proposed methods offer a significant improvement for image understanding and scene classification.