Object detection in quantized feature space

In this paper, we present a method for the detection of objects in a quantized feature space. Quantizing the feature space is a preprocessing step to compact the amount of data in large scale image retrieval and classification applications. A drawback, compared to the use of non-quantized features, is the loss in the ability to precisely detect and localize common objects across the images. Our method can handle this limitation and is based on the frequently used Bag of Visual Keypoints representation in combination with a sliding window approach. Thereby, it does not need any knowledge about the objects. Experiments on real and synthetic images show the good performance of our approach.