The Internet contains billions of images, freely available online. Methods for efficiently searching this incredibly rich resource are vital for a large number of applications. These include object recognition [2] , computer graphics [11, 27] Recent interest in object recognition has yielded a wide range of approaches to describing the contents of an image. One important application for this technology is the visual search of large collections of images, such as those on the Internet or on people's home computers. Accordingly, a number of recognition papers have explored this area. Nister and Stewenius demonstrate the real-time specific object recognition using a database of 40,000 images [19] ; Obdrzalek and Matas show sub-linear indexing time on the COIL dataset [20] . A common theme is the representation of the image as a collection of feature vectors and the use of efficient data structures to handle the large number of images. These ideas are common to many approaches in the content based image retrieval (CBIR) community, although the emphasis on really large datasets means that the chosen im- Our approach is based on binary codes for representing images and their neighborhood structure. Such codes have received limited attention in both the vision and CBIR communities. Ghosh et al. [7] use them to find duplicate images in a database. Binary codes have also been used to represent the color of an image [13, 18] . Landre et al. [14] use color, texture and shape cues in a 32-bit vector to perform retrieval on a 10,000 image dataset. These approaches also use manually designed descriptors, which in view of the tiny capacity of each code, is likely to be highly suboptimal particularly when the database is large, a scenario not investigated by any of these papers. Unlike CBIR we seek to recognize the objects present 1 978-1-4244-2243-2/08/$25.00 ©2008 IEEE
Similar deep learning hash methods have also been applied to the task of image retrieval in very large databases REF .
Small codes and large image databases for recognition
2008 IEEE Conference on Computer Vision and Pattern Recognition
2008 IEEE Conference on Computer Vision and Pattern Recognition
Computer Science