Reduction of Semantic Gap in Image Retrieval by Improving the Effectiveness of Fusion in Manifold Learning

Content based image retrieval (CBIR) means image retrieval by low level features such as color, texture and shape. In this way, semantic gap is defined as the difference of image interpretation between human and computer algorithm. In this domain, incorrect mapping of low-level features to high-level semantics leads to widening in semantic gap. In image retrieval it is possible to texture, color, and shape of image are changed but the concept is not transitioned in human mind. However, in most cases, feature vector of image is moved in feature space and therefore image is not correctly retrieved. The purpose of this paper is reducing the dimensions of feature vectors by a non-linear approach, learning the manifold space and developing a new feature vector to coincide distances in semantic and feature space domains.So, the continuity between the instances of a semantic at the semantic space is kept in feature space. The main innovation of this paper is extraction of one feature space from multiple ones. In the proposed manner, adverse effect of noise in manifold learning is decreased. Experiments are done on MPEG-7 Part B and Fish datasets and results show effectiveness of proposed methd.