Efficient Bag of Words Based Concept Extraction for Visual Object Retrieval

Abstract

Recent burst of multimedia content available on Internet is pushing expectations on multimedia retrieval systems to even higher grounds. Multimedia retrieval systems should offer better performance both in terms of speed and memory consumption while maintaining good accuracy compared to state-of-the-art implementations. In this paper, we discuss alternative implementations of visual object retrieval systems based on popular bag of words model and show optimal selection of processing steps. We demonstrate our offering using both keyword and example-based retrieval queries on three frequently used benchmark databases, namely Oxford, Paris and Pascal VOC 2007. Additionally, we investigate effect of different distance comparison metrics on retrieval accuracy. Results show that, relatively simple but efficient vector quantization can compete with more sophisticated feature encoding schemes together with the adapted inverted index structure.