Name
Abstract | ||
|---|---|---|
In the past, quantized local descriptors have been shown to be a good base for the representation of images, that can be applied to a wide range of tasks. However, current approaches typically consider only one level of quantization to create the final image representation. In this view they somehow restrict the image description to one level of visual detail. We propose to build image representations from multi-level quantization of local interest point descriptors, automatically extracted from the images. The use of this new multi-level representation will allow for the description of fine and coarse local image detail in one framework. To evaluate the performance of our approach we perform scene image classification using a 13-class data set. We show that the use of information from multiple quantization levels increases the classification performance, which suggests that the different granularity captured by the multi-level quantization produces a more discriminant image representation. Moreover, by using a multi-level approach, the time necessary to learn the quantization models can be reduced by learning the different models in parallel. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1145/1282280.1282319 | CIVR |
Keywords | Field | DocType |
coarse local image detail,multi-level approach,final image representation,scene image classification,discriminant image representation,bag-of-visterms image representation,multi-level local descriptor quantization,multiple quantization level,image description,multi-level quantization,quantization model,image representation,quantization,vision,image classification,bag of words | Bag-of-words model,Computer vision,Pattern recognition,Feature detection (computer vision),Discriminant,Computer science,Artificial intelligence,Quantization (physics),Granularity,Quantization (signal processing),Contextual image classification,Color quantization | Conference |
Citations | PageRank | References |
6 | 0.44 | 21 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Pedro Quelhas | 1 | 261 | 21.51 |
| Jean-marc Odobez | 2 | 1641 | 110.52 |