Toward a higher-level visual representation for content-based image retrieval

Ismail Elsayad (1) , Jean Martinet (1) , Thierry Urruty (2) , Chabane Djeraba (1)

1 LIFL - FOX MIIRE
2 XLIM-SIC - SIC

Having effective methods to access the desired images is essential nowadays with the availability of a huge amount of digital images. The proposed approach is based on an analogy between content-based image retrieval and text retrieval. The aim of the approach is to build a meaningful mid-level representation of images to be used later on for matching between a query image and other images in the desired database. The approach is based firstly on constructing different visual words using local patch extraction and fusion of descriptors. Secondly, we introduce a new method using multilayer pLSA to eliminate the noisiest words generated by the vocabulary building process. Thirdly, a new spatial weighting scheme is introduced that consists of weighting visual words according to the probability of each visual word to belong to each of the n Gaussian. Finally, we construct visual phrases from groups of visual words that are involved in strong association rules. Experimental results show that our approach outperforms the results of traditional image retrieval techniques.

Keywords: Multimedia Information Retrieval

Domains: Information Retrieval [cs.IR]; Image Processing [eess.IV]

Ismail Elsayad, Jean Martinet, Thierry Urruty, Chabane Djeraba. Toward a higher-level visual representation for content-based image retrieval. Multimedia Tools and Applications, 2012, 60 (2), pp.455-482. ⟨10.1007/s11042-010-0596-x⟩. ⟨hal-00876204⟩