image indexing, namely, the problem of retrieving content information from images in response to queries, is a key problem underlying several operations in image databases. Indexing for object queries, in particular, is a difficult problem, as it requires localizing an unanticipated object in unsegmented images. This inevitably involves search, a computationally intensive operation when based entirely on image features. It is desirable to have efficient data structures that avoid the need for sequential search through images and their features for query localization. Conventional data structures used for database organization are not adequate for image indexing where the object query has to be located in images depicting changed imaging conditions that include pose changes and occlusions. In this paper, we explore the use of a geometric hash table as a suitable data structure for fast image indexing. The technique of geometric hashing has been used in computer vision for indexing a library of models to find candidate model objects for recognition in the isolated image region. Here, however, we use geometric hashing as a technique of fast query localization in unsegmented images of a database. Specifically, we show that by using three consecutive features along a curve as basis points for affine invariance, a hash table can be constructed for images that is quadratic in the number of features. The resulting indexing method is also quadratic in the number of features. The query localization by geometric hashing is demonstrated for the problem of indexing of handwritten documents based on handwriting pattern queries.