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arXiv · 1705.07420

Large-Scale Classification of Structured Objects using a CRF with Deep Class Embedding

Eran Goldman·Jacob Goldberger
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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.

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BibTeXRIS

Eran Goldman, Jacob Goldberger. 2017-05-21. Large-Scale Classification of Structured Objects using a CRF with Deep Class Embedding. https://doi.org/10.1016/j.cviu.2019.102865

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