Content based image retrieval using fusion of multilevel bag of visual words

Abstract

Content based image retrieval (CBIR) is the art of finding visually and conceptually similar pictures to the given query picture. Usually, there is a semantic gap between low-level image features and high-level concepts perceived by viewers. Although features such as intensity and color enforce a good distinction between the images in terms of greater detail, they convey little semantic information. Therefore, employing higher-level features such as properties of regions and objects within the image could improve the retrieval performance. In this study, features are extracted at the pixel, region, object, and concept levels. The fusion step concatenates the four feature vectors and maps it to a lower-dimensional space using auto-encoders. The experiments confirm the efficiency of the proposed method over the individual feature groups and also the state of the art methods.