Large-Scale Classification of Structured Objects using a CRF with Deep Class Embedding
arXiv:1705.07420 · doi:10.1016/j.cviu.2019.102865
Abstract
This paper presents a novel deep learning architecture to classify structured objects in datasets with a large number of visually similar categories. We model sequences of images as linear-chain CRFs, and jointly learn the parameters from both local-visual features and neighboring classes. The visual features are computed by convolutional layers, and the class embeddings are learned by factorizing the CRF pairwise potential matrix. This forms a highly nonlinear objective function which is trained by optimizing a local likelihood approximation with batch-normalization. This model overcomes the difficulties of existing CRF methods to learn the contextual relationships thoroughly when there is a large number of classes and the data is sparse. The performance of the proposed method is illustrated on a huge dataset that contains images of retail-store product displays, taken in varying settings and viewpoints, and shows significantly improved results compared to linear CRF modeling and unnormalized likelihood optimization.
References in corpus (6)
- Very Deep Convolutional Networks for Large-Scale Image Recognition
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
- Object-Part Attention Model for Fine-grained Image Classification
- Learning to Compare Image Patches via Convolutional Neural Networks
- Piecewise Training for Undirected Models
- A Fully Convolutional Deep Auditory Model for Musical Chord Recognition