Categorizing scenes by exploring scene part information without constructing explicit models

5 years ago

Categorizing scenes by exploring scene part information without constructing explicit models

Approaches based on scene parts are deemed to be one of the main streams for scene categorization. In previous methods, before one can utilize scene parts, models need to be constructed for them first. The quality of part models has a great influence on the final results. However, building high-quality scene part models is still an open question. To perform scene categorization based on parts effectively, in this paper we propose to explore scene part information without constructing explicit models for them. For this purpose, a cascading framework is used, at each of whose stages we aim to process image patches potentially corresponding to scene parts from different perspectives. Specifically, the first stage of the framework uses the selective search algorithm to extract possible object patches from images and represents obtained patches based on convolutional neural networks. Then, spectral clustering and linear support vector machines are adopted to select representative visual patterns for images in the second stage. In the third stage, random forest and multi-class support vector machines are combined to mine and classify image features for determining the categories of the images. Through using the cascading framework, we can explore scene part information step by step without needing to construct explicit models for them. Finally, extensive experiments are conducted to evaluate the proposed method on three well-known benchmark scene datasets, i.e. MIT Indoor 67, SUN397 and Places. Experiment results demonstrated the effectiveness of the proposed method.

Publisher URL: www.sciencedirect.com/science

DOI: S0925231217318301