A Survey on Entropy Optimized Feature-based Bag-of-Words Representation for Information Retrieval
Abstract: In this paper, we present a supervised dictionary learning method for improving the component based Bag-of-Words (BoW) representation towards Information Retrieval. Taking after the bunch theory, which expresses that focuses in a similar group are probably going to satisfy a similar data require, we propose the utilization of an entropy-based enhancement basis that is more qualified for recovery of order. We show the capacity of the proposed strategy, curtailed as EO-BoW, to enhance the recovery execution by giving broad analyses on two multi-class picture datasets. The BoW model can be connected to different spaces too, so we additionally assess our approach utilizing a gathering of 45 time-arrangement datasets, a content dataset and a video dataset. The increases are three-crease since the EO-BoW can enhance the mean Average Precision, while decreasing the encoding time and the database stockpiling necessities. At long last, we give prove that the EO-BoW keeps up its representation capacity notwithstanding when used to recover objects from classes that were not seen amid the preparation.
Keywords: Entropy optimized, Bag-of-words, Information Retrieval
How to Cite?: Swaroop Kale, H. A. Hingoliwala, "A Survey on Entropy Optimized Feature-based Bag-of-Words Representation for Information Retrieval", Volume 6 Issue 1, January 2017, International Journal of Science and Research (IJSR), Pages: 1173-1178, https://www.ijsr.net/getabstract.php?paperid=ART20164280, DOI: https://dx.doi.org/10.21275/ART20164280