Sketch to digital image matching refers to the problem of matching a sketch image (often drawn by hand or created by a software) against a gallery of digital images (captured via an acquisition device such as a digital camera). Automated sketch to digital image matching has applicability in several day to day tasks such as similar object image retrieval, forensic sketch matching in law enforcement scenarios, or profile linking using caricature face images on social media. As opposed to the digital images, sketch images are generally edge-drawings containing limited (or no) textural or colour based information. Further, there is no single technique for sketch generation, which often results in varying artistic or software styles, along with the interpretation bias of the individual creating the sketch. Beyond the variations observed across the two domains (sketch and digital image), automated sketch to digital image matching is further marred by the challenge of limited training data and wide intra-class variability. In order to address the above problems, this research proposes a novel Discriminative Shared Transform Learning (DSTL) algorithm for sketch to digital image matching. DSTL learns a shared transform for data belonging to the two domains, while modeling the class variations, resulting in discriminative feature learning. Two models have been presented under the proposed DSTL algorithm: (i) Contractive Model (C-Model) and (ii) Divergent Model (D-Model), which have been formulated with different supervision constraints. Experimental analysis on seven datasets for three case studies of sketch to digital image matching demonstrate the efficacy of the proposed approach, highlighting the importance of each component, its input-agnostic behavior, and improved matching performance.

草图与数字图像的匹配是指将草图图像（通常是手工绘制或由软件创建）与数字图像库（通过诸如数码相机的采集设备捕获）相匹配的问题。草图与数字图像的自动匹配在某些日常任务中具有适用性，例如类似的对象图像检索，执法场景中的法医草图匹配或使用社交媒体上的漫画面部图像进行个人资料链接。与数字图像相反，草图图像通常是包含有限（或不包含）基于纹理或颜色的信息的边缘绘图。此外，没有单一的草图生成技术，通常会导致艺术风格或软件风格的变化，以及创建草图的人的解释偏见。除了在两个域（草图和数字图像）上观察到的变化之外，自动训练草图到数字图像的匹配还受到训练数据有限和组内差异大的挑战的损害。为了解决上述问题，本研究提出了一种新颖的草图到数字图像匹配的区别性共享变换学习（DSTL）算法。DSTL在对类变化进行建模的同时，学习了属于这两个域的数据的共享转换，从而实现了有区别的特征学习。在建议的DSTL算法下，已经提出了两个模型：（i）收缩模型（C-模型）和（ii）发散模型（D-模型），它们是根据不同的监督约束制定的。对草图到数字图像匹配的三个案例研究的七个数据集的实验分析证明了该方法的有效性，强调了每个组件的重要性，其输入不可知行为以及改进的匹配性能。