A Saliency Detection Based Unsupervised Commodity Object Retrieval Scheme

Commodity object retrieval is a key issue in the application of self-service shopping and so on. In this paper, we propose a saliency object detection-based unsupervised commodity object retrieval scheme. Since most commodity objects are conspicuous and not complicated in commodity images, saliency detection could predict a saliency box that indicates approximate position information of objects. The proposed scheme utilizes the saliency box to filter the proposals extracted by selective search. The reserved proposals have a big overlapping ratio with saliency box to a large extent. This paper composes both the saliency box and the reserved proposals as saliency proposals. Furthermore, we propose a channel weighting generalized mean pooling feature to represent saliency proposals. On one hand, the reduction of proposals’ number after filtering significantly improves the computational efficiency; on the other hand, the new feature more accurately represents the objects to be retrieved, which results in higher retrieval precision. In addition, we built and manually annotated a commodity data set named PRODUCT to evaluate the proposed method. Extensive experiments are also conducted on the databases INSTRE and Flick32. The results demonstrate the superior performance of our scheme compared with the other state-of-the-art methods.