Human beings recognize similarity in scene perception based on their available high-level knowledge about the low-level visual features, which is gradually accumulated throughout their entire lives. Once there is not enough knowledge they tend to rely on low-level visual content. Inspired by this observation, we proposed a new framework of relevance feedback for content-free image retrieval to tackle the problem of sample sparseness. The framework is composed of two components, i.e. short-term feedback and long-term feedback. The former refers to an operation of query conversion and/or refinement during a retrieval session by incorporating a content-aware module, while the latter consists of incrementally updating the system model using the accumulated retrieval results since the last system update. 10000 images from 200 categories of the COREL image collection were employed for evaluating the performance of the framework using the criterion of averaged precision as a function of the number of relevance feedback needed. Experimental results demonstrated a human-like behavior of the proposed framework in that while long-term update helps the system accumulate more knowledge, the content-aware short-term relevance feedback further boosts its performance when the amount of knowledge is limited.
['Rui Zhang', 'Ling Guan']
A new framework of relevance feedback for content-free image retrieval