Photo Shot-Type Disambiguation by Multi-Classifier Semi-Supervised Learning
Yang Liu, Xuefeng Liang, Song Tong, Takatsune Kumada · 2019
The previous studies showed that classifying photo shot-types into either narrow-type or wide-type was non-trivial because of ambiguous photos. To disambiguate such data, we define this problem as learning an ambiguity distribution instead of the conventional binary label, and then propose a multi-classifier semi-supervised learning framework (MCSSL) to tackle two issues: (1) Commonly, the ambiguity distribution of such photos are unavailable in practice. By introducing semi-supervised learning to multiple different classifiers (DNNs), MCSSL can mimic the inconsistent recognitions among multiple sources to determine the ambiguous degrees automatically for a photo. (2) The widely accepted Cross-Entropy loss is unfeasible for ambiguity learning. Instead, MCSSL applies the Kullback-Leibler divergence for DNNs to measure the similarity between the predicted distribution and the one given by multiple sources. The experiments on 122K photos demonstrate that MCSSL raises the classification accuracy of all DNNs, and improve the recognition consistency among them from substantial to almost perfect, which is our goal of data disambiguation.