Centroid-Based Scene Classification (CBSC): Using Deep Features and Clustering for RGB-D Indoor Scene Classification
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This paper contributes a novel method for RGB-D indoor scene classification. Recent approaches to this problem focus on developing increasingly complex pipelines that learn correlated features across the RGB and depth modalities. In contrast, this paper presents a simple method that first extracts features for the RGB and depth modalities using Places365-CNN and fine-tuned Places365-CNN on depth data, respectively and then clusters these features to generate a set of centroids representing each scene category from the training data. For classification a scene image is converted to CNN features and the distance of these features to the n closest learned centroids is used to predict the image's category. We evaluate our method on two standard RGB-D indoor scene classification benchmarks: SUNRGB-D and NYU Depth V2 and demonstrate that our proposed classification approach achieves superior performance over the state-of-the-art methods on both datasets.
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