Section: Research Program

Image description

In most contexts where images are to be compared, a direct comparison is impossible. Images are compressed in different formats, most formats are error-prone, images are re-sized, cropped, etc. The solution consists in computing descriptors, which are invariant to these transformations.

The first description methods associate a unique global descriptor with each image, e.g., a color histogram or correlogram, a texture descriptor. Such descriptors are easy to compute and use, but they usually fail to handle cropping and cannot be used for object recognition. The most successful approach to address a large class of transformations relies on the use of local descriptors, extracted on regions of interest detected by a detector, for instance the Harris detector  [87] or the Difference of Gaussian method proposed by David Lowe  [89] .

The detectors select a square, circular or elliptic region that is described in turn by a patch descriptor, usually referred to as a local descriptor. The most established description method, namely the SIFT descriptor  [89] , was shown robust to geometric and photometric transforms. Each local SIFT descriptor captures the information provided by the gradient directions and intensities in the region of interest in each region of a 4$\times$4 grid, thereby taking into account the spatial organization of the gradient in a region. As a matter of fact, the SIFT descriptor has became a standard for image and video description.

Local descriptors can be used in many applications: image comparison for object recognition, image copy detection, detection of repeats in television streams, etc. While they are very reliable, local descriptors are not without problems. As many descriptors can be computed for a single image, a collection of one million images generates in the order of a billion descriptors. That is why specific indexing techniques are required. The problem of taking full advantage of these strong descriptors on a large scale is still an open and active problem. Most of the recent techniques consists in computing a global descriptor from local ones, such as proposed in the so-called bag-of-visual-word approach  [96] . Recently, global description computed from local descriptors has been shown successful in breaking the complexity problem. We are active in designing methods that aggregate local descriptors into a single vector representation without loosing too much of the discriminative power of the descriptors.