Deep CNN based pseudo-concept selection and modeling for generation of semantic multinomial representation of scene images (original) (raw)

Proceedings of the ACM India Joint International Conference on Data Science and Management of Data, 2018

Abstract

Though recent convolutional neural network (CNN) based method for scene classification task show impressive results but lacks in capturing the complex semantic content of the scene images. To reduce the semantic gap a semantic multinomial (SMN) representation is introduced. SMN representation corresponds to a vector of posterior probabilities of concepts. The core part of SMN generation is building the concept model. For building the concept model, it is necessary to have ground truth (true) concept labels for every image in the database. In this research work, we propose novel deep CNN based SMN representation which exploits convolutional layer filters response as pseudo concepts to build the concept model in the absence of true concept labels. The effectiveness of the proposed approach is studied for scene classification tasks on standard datasets like MIT67 and SUN397.

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