Content-based image retrieval with pachinko allocation model and a combination of colour, texture and text features

We present in this paper a content-based image retrieval system (CBIR) based on pachinko allocation model (PAM) and employing a combination of colour, texture and textual features. PAM is a probabilistic topic model which captures correlation not only between words in documents but also between different topics (concepts) responsible of their generation. Regardless this advantage of PAM, there is no work which explores its utility for content based image retrieval tasks. We aim to evaluate the use of PAM for CBIRs by implementing a system based on it. In this context, PAM was applied with two different modalities of features, image global features and textual indexes separately and combined. Mean average precision is evaluated. The use of PAM with combination of features has slightly improved results of using it with just one modality, this opens more perspective in order to enhance results.