A survey of extended methods to the bag of visual words for image categorization and retrieval

The semantic gap is a crucial issue in the enhancement of computer vision. The user longs for retrieving images on a semantic level, but the image characterizations can only give a low-level similarity. As a result, recording a stage medium between high-level semantic concepts and low-level visual features is a stimulating task. A recent work, called Bag of visual Words (BoW) have arisen to resolve this difficulty in greater generality through the conception of techniques genius relevantly learning semantic vocabularies. In spite of its clarity and effectiveness, the building of a codebook is a critical step which is ordinarily performed by coding and pooling step. Yet, it is still difficult to build a compact codebook with shortened calculation cost. For that, several approaches try to overcome these difficulties and to improve image representation. In this paper, we introduce a survey investigates to cover the inadequacy of a full description of the most important public approaches for image categorization and retrieval.