Mélange fabric image retrieval based on soft similarity learning

Abstract

Fabric image retrieval, a special case in Content Based Image Retrieval, has high potential application value in many fields. Compared with common image retrieval, fabric image retrieval has high requirements for results. To address the actual needs of the industry for Mélange fabric retrieval, we propose a novel framework for efficient and accurate fabric retrieval. We first introduce a quantified similarity definition, soft similarity, to measure the fine-grained pairwise similarity and design a CNN for fabric image representation. An objective function, which consists of three losses: soft similarity loss for preserving the similarity, cross-entropy loss for image representation, and quantization loss for controlling the quality of hash code, is used to drive the learning of the model. Experimental results demonstrate that the proposed method can not only achieve effective feature learning and hashing learning, but also effectively work on smaller datasets. Comparative experiments illustrate that the proposed method outperforms the compared methods.