Bag of Features Model Using the New Approaches: A Comprehensive Study

The major challenge in content based image retrieval is the semantic gap. Images are described mainly on the basis of their numerical information, while users are more interested in their semantic content and it is really difficult to find a correspondence between these two sides. The bag of features (BoF) model is an efficient image representation technique for image classification. However, it has some limitations for instance the information loss during the encoding process, an important step of BoF. This is because the encoding is usually done by hard assignment i.e. in vector quantization each feature is encoded by being assigned to a single visual word. Another notorious disadvantage of BoF is that it ignores the spatial relationships among the patches, which are very important in image representation. To address those limitations and enhance the results, novel approaches were proposed at each level of the BoF pipeline. In instance the combination of local and global descriptors for a better description, a soft-assignment encoding manner with a spatial pyramid partitioning for a more informative image representation and a maximum pooling to get the final descriptors. Our work aims to give a detailed version of the BoF, including all the levels of the pipeline, as a support leading to a better comprehension of the approach. We also compare and evaluate the state-of-the-art approaches and find out how these changes at each level of the pipeline could affect the performance and the overall classification results.