Effective object-based image retrieval using higher-level visual representation

Having effective methods to access the desired images is essential nowadays with the availability of huge amount of digital images. The proposed approach is based on an analogy between image retrieval containing desired objects (object-based image retrieval) and text retrieval. We propose a higher-level visual representation, for object-based image retrieval beyond visual appearances. The proposed visual representation improves the traditional part-based bag-of-words image representation, in two aspects. First, the approach strengthens the discrimination power of visual words by constructing an mid level descriptor, visual phrase, from frequently co-occurring and non noisy visual word-set in the same local context. Second, to bridge the visual appearance difference or to achieve better intra-class invariance power, the approach clusters visual words and phrases into visual sentence, based on their class probability distribution.