Egg pattern plays a critical role in avian brood parasitism where a brood parasite exploits a host's parental care by laying eggs in host nests relying on the host to rear the parasitic egg(s). It has been demonstrated that many hosts have evolved an ability to recognize and reject parasitic eggs, based on differences in egg patterns, as a defense against brood parasitism, while some parasites have evolved egg mimicry to counter the host defense. Egg pattern is a complex phenotype and its quantitative evaluation has been a focus of much research. In this paper, we propose a novel method for quantifying an egg pattern and assessing the degree of egg pattern mimicry—a measure of the similarity of a parasite egg to host eggs. Our approach is based on image analysis focused on local binary pattern (LBP) and its variant completed local binary pattern (CLBP) that captures the local structure of a pattern in an image. We compare the results obtained by LBP and CLBP with human-eye evaluation, a classical method widely used in previous studies. Both LBP and CLBP can successfully assess the similarity in egg pattern that is positively correlated with human-eye scores with a high accuracy rate. LBP tends to perform better than CLBP at small scales while CLBP performs better for a wide range of larger scales. Our method can be an effective alternative means of assessing the degree of egg pattern mimicry objectively, providing a useful tool for biologists studying avian brood parasitism. Many bird species have specific eggshell patterns (e.g. spots, blotches, lines) and it has been suggested that these patterns are an important functional trait, such as for camouflage against predators. We propose our method as a useful and objective tool for assessing egg patterns.