Part-based annotation-free fine-grained classification of images of retail products

Article Type

Research Article

Publication Title

Pattern Recognition


We propose a novel solution that classifies very similar images (fine-grained classification) of variants of retail products displayed on the racks of supermarkets. The proposed scheme simultaneously captures object-level and part-level cues of the product images. The object-level cues of the product images are captured with our novel reconstruction-classification network (RC-Net). For annotation-free modeling of part-level cues, the discriminatory parts of the product images are identified around the keypoints. The ordered sequences of these discriminatory parts, encoded using convolutional LSTM, describe the products uniquely. Finally, the part-level and object-level models jointly determine the products explicitly explaining coarse to finer descriptions of the products. This bi-level architecture is embedded in R-CNN for recognizing variants of retail products on the rack. We perform extensive experiments on one In-house and three benchmark datasets. The proposed scheme outperforms competing methods in almost all the evaluations.



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