Traditional object recognition algorithms are based on a commonly adopted closed-set hypothesis, assuming that the knowledge given in training is complete. However, real situations are often open and nonstatic, in which case the models only obtain incomplete knowledge during the training phase. This paper proposes a new type of conditional random field (CRF) model to solve a special case of incomplete knowledge, in which the visual appearance of certain objects changes significantly between training and testing, and as a result, certain unary features (features of individual objects) extracted from red green blue-depth (RGB-D) images are no longer reliable. Mirror nodes are introduced into the architecture based on the standard CRF model to build the mirrored conditional random field (Mirror-CRF) model, which integrates two types of object nodes: original nodes and mirror nodes. The mirror nodes have no unary features, only pairwise features, which describe relationships between two objects and are more reliable than unary features for object recognition in the case of appearance variation. The experimental results show that the Mirror-CRF model reduces the influence of significant changes in the appearance of certain objects and improves the object recognition ability under the condition of incomplete knowledge.

假设训练中提供的知识是完整的，传统的物体识别算法是基于普遍采用的封闭集假设的。但是，实际情况通常是公开的且是非静态的，在这种情况下，模型仅在训练阶段获得不完整的知识。本文提出了一种新型的条件随机场（CRF）模型，以解决特殊情况下知识不完全的情况，在这种情况下，某些对象的视觉外观在训练和测试之间发生了显着变化，因此，某些一元特征（个体特征从红绿蓝深度（RGB-D）图像中提取的对象不再可靠。将镜像节点引入基于标准CRF模型的体系结构中，以构建镜像条件随机字段（Mirror-CRF）模型，该模型集成了两种类型的对象节点：原始节点和镜像节点。镜像节点没有一元特征，只有成对特征，它描述了两个对象之间的关系，并且在外观变化的情况下比一元特征更可靠地识别对象。实验结果表明，Mirror-CRF模型在知识不完全的情况下，减小了某些物体外观的重大变化的影响，提高了物体的识别能力。