Distribution Entropy Boosted VLAD for Image Retrieval

• Main Authors: Qiuzhan Zhou, Cheng Wang, Pingping Liu, Qingliang Li, Yeran Wang, Shuozhang Chen
• Format: Article
• Language: English
• Published: MDPI AG 2016-08-01
• Series: Entropy
• Subjects: image retrieval; VLAD; distribution entropy; quantization error; normalization
• Online Access: http://www.mdpi.com/1099-4300/18/8/311

Several recent works have shown that aggregating local descriptors to generate global image representation results in great efficiency for retrieval and classification tasks. The most popular method following this approach is VLAD (Vector of Locally Aggregated Descriptors). We present a novel image presentation called Distribution Entropy Boosted VLAD (EVLAD), which extends the original vector of locally aggregated descriptors. The original VLAD adopts only residuals to depict the distribution information of every visual word and neglects other statistical clues, so its discriminative power is limited. To address this issue, this paper proposes the use of the distribution entropy of each cluster as supplementary information to enhance the search accuracy. To fuse two feature sources organically, two fusion methods after a new normalization stage meeting power law are also investigated, which generate identically sized and double-sized vectors as the original VLAD. We validate our approach in image retrieval and image classification experiments. Experimental results demonstrate the effectiveness of our algorithm.