Unsupervised feature approach for content based image retrieval using principal component analysis

In recent years, there are available extremely large collections of images located on distributed and heterogeneous platforms over the online web service. The proliferation of digital cameras and the growing photo sharing using current technology for browsing such collections, but at the same time it spurred the emergence of new image retrieval techniques based not only on photos' visual information, but on geo-location tags. Currently image retrieval systems; the retrieval process is performed using similarity strategies applied on certain features in the image. In this paper, we proposed a process of image refining retrieval result by exploiting and fusing unsupervised feature technique Principal component analysis (PCA) and spectral clustering. PCA algorithm is used for to remove the outliers from the initially retrieved image set, and then it uses Principal Component Analysis (PCA) to extract principal components of the feature values. Later on, feature values of each image are exhibited by a linear combination of these principal components. Spectral clustering analyzes retrieval process by clustering together visually similar images. PCA and spectral clustering require manual turning of their parameters, which usually requires a priori knowledge of the dataset. To overcome this problem we developed a tuning mechanism that automatically tunes the parameters of both algorithms. For the evaluation of the proposed approach we used thousands of images from Flickr downloaded using text queries for well-known cultural heritage monuments. The proposed method was performed and tested on a set of images from variant sceneries. Experimental results show the superior performance of this approach.