Stopping Rules for Bag-of-Words Image Search and Its Application in Appearance-Based Localization
This work addresses efficiency for image retrieval systems, but it is incremental as it builds on existing bag-of-words methods.
The paper tackles the problem of computational inefficiency in bag-of-words image search by introducing stopping rules based on query difficulty, reducing computational cost significantly in appearance-based localization.
We propose a technique to improve the search efficiency of the bag-of-words (BoW) method for image retrieval. We introduce a notion of difficulty for the image matching problems and propose methods that reduce the amount of computations required for the feature vector-quantization task in BoW by exploiting the fact that easier queries need less computational resources. Measuring the difficulty of a query and stopping the search accordingly is formulated as a stopping problem. We introduce stopping rules that terminate the image search depending on the difficulty of each query, thereby significantly reducing the computational cost. Our experimental results show the effectiveness of our approach when it is applied to appearance-based localization problem.