This study investigates the visual place retrieval of an image query using a geotagged image dataset. Vector of Locally Aggregated Descriptors (VLAD) is one of the local features that can be used for image place recognition. VLAD describes an image by the difference of its local feature descriptors from an already computed codebook. Generally, a visual codebook is generated from k-means clustering of the descriptors. However, the dimensionality of visual features is not trivial and the computational load of sample distances in a large image dataset is challenging. In order to design an accurate image retrieval method with affordable computation expenses, we propose to use the mini-batch k-means clustering to compute VLAD descriptor(MB-VLAD). The proposed MB�VLAD technique shows advantage in retrieval accuracy in comparison with the state of the art techniques.