Issue No. 02 - Feb. (2013 vol. 25)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TKDE.2011.219
Konstantinos A. Raftopoulos , National Technical University of Athens, Athens
Klimis S. Ntalianis , National Technical University of Athens, Athens
Dionyssios D. Sourlas , Elais-Unilever Hellas S.A, Athens
Stefanos D. Kollias , National Technical University of Athens, Athens
We propose a novel method for automatic annotation, indexing and annotation-based retrieval of images. The new method, that we call Markovian Semantic Indexing (MSI), is presented in the context of an online image retrieval system. Assuming such a system, the users' queries are used to construct an Aggregate Markov Chain (AMC) through which the relevance between the keywords seen by the system is defined. The users' queries are also used to automatically annotate the images. A stochastic distance between images, based on their annotation and the keyword relevance captured in the AMC, is then introduced. Geometric interpretations of the proposed distance are provided and its relation to a clustering in the keyword space is investigated. By means of a new measure of Markovian state similarity, the mean first cross passage time (CPT), optimality properties of the proposed distance are proved. Images are modeled as points in a vector space and their similarity is measured with MSI. The new method is shown to possess certain theoretical advantages and also to achieve better Precision versus Recall results when compared to Latent Semantic Indexing (LSI) and probabilistic Latent Semantic Indexing (pLSI) methods in Annotation-Based Image Retrieval (ABIR) tasks.
Semantics, Markov processes, Image retrieval, Convergence, Indexing, Probabilistic logic, Eigenvalues and eigenfunctions, annotation-based image retrieval, Markovian semantic indexing, image annotation, query mining
S. D. Kollias, D. D. Sourlas, K. S. Ntalianis and K. A. Raftopoulos, "Mining User Queries with Markov Chains: Application to Online Image Retrieval," in IEEE Transactions on Knowledge & Data Engineering, vol. 25, no. , pp. 433-447, 2013.