Latent Dirichlet allocation is the prevalent topic model and performs well for image classification. However, it ignores visual word spatial information, which affects topic assignment accuracy. This paper proposes an effective topic model framework based on spatial pyramids including visual word regional information: spatial topic pyramid model (STPM). STPM divides the images into different scale regions and uses the regional topic distributions to represent the images. The regional topic distributions effectively represent image characteristics, because they include global information (regarding the image as a single region) and the regional relationships of visual words in different scale regions. Since the pyramid layers are independent, different topic models and parameters can be used for different scale layers. It makes STPM flexible and easily extensible.
As a representative deep learning model, convolutional neural networks (CNNs) have accomplished great achievements in image classification and object detection. However, CNNs require the resizing of the input images to a fixed size, which may affect the representations of objects. To overcome this limitation, we replace the last pooling layer with a topic model and call it a topic network. For arbitrary sizes and ratios of input images, the outputs of the topic network are fixed-size features due to the topic model of the topic layer, and they can reflect the global or regional characteristics of images by means of different scales. Two topic models, namely latent Dirichlet allocation (LDA) and Markov topic random fields (MTRF), are applied to the topic layer, and we call them latent Dirichlet allocation topic network and Markov topic random fields topic network, respectively. Both of them perform well in image classification with original size. More importantly, as a framework, any topic model can be easily applied to the topic layer of a topic network, which makes it much more flexible and extensible.
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