In the world of big data, large amounts of images are available in social media, corporate and even personal collections. A collection may grow quickly as new images are generated at high rates. The new images may cause changes in the distribution of existing classes or the emergence of new classes, resulting in the collection being dynamic and having concept drift. For efficient image retrieval from an image collection using a query, a hash table consisting of a set of hash functions is needed to transform images into binary hash codes which are used as the basis to find similar images to the query. If the image collection is dynamic, the hash table built at one time step may not work well at the next due to changes in the collection as a result of new images being added. Therefore, the hash table needs to be rebuilt or updated at successive time steps. Incremental hashing (ICH) is the first effective method to deal with the concept drift problem in image retrieval from dynamic collections. In ICH, a new hash table is learned based on newly emerging images only which represent data distribution of the current data environment. The new hash table is used to generate hash codes for all images including old and new ones. Due to the dynamic nature, new images of one class may not be similar to old images of the same class. In order to learn new hash table that preserves within-class similarity in both old and new images, incremental hashing with sample selection using dominant sets (ICHDS) is proposed in this paper, which selects representative samples from each class for training the new hash table. Experimental results show that ICHDS yields better retrieval performance than existing dynamic and static hashing methods.

在大数据世界中，社交媒体，公司甚至个人收藏中都可以找到大量图像。随着高速率生成新图像，集合可能会迅速增长。新图像可能会导致现有类的分布发生变化或新类的出现，从而导致集合是动态的，并且概念漂移。为了使用查询从图像集合中高效检索图像，需要由一组哈希函数组成的哈希表才能将图像转换为二进制哈希码用作查找与查询相似的图像的基础。如果图像集合是动态的，则由于添加了新图像而导致集合中的更改，因此在一个时间步骤上构建的哈希表在下一个步骤中可能无法正常工作。因此，哈希表需要在连续的时间步长处重建或更新。增量哈希（ICH）是处理从动态集合检索图像时概念漂移问题的第一种有效方法。在ICH中，仅基于新出现的图像学习新的哈希表，这些图像代表当前数据环境的数据分布。新的哈希表用于为所有图像（包括旧图像和新图像）生成哈希码。由于动态性质，一类的新图像可能与同类的旧图像不相似。本文提出了使用显性集（ICHDS）进行样本选择的增量哈希，该算法从每个类别中选择代表性样本以训练新哈希表。实验结果表明，与现有的动态和静态哈希算法相比，ICHDS的检索性能更好。