A framework for the revision of large-scale image retrieval benchmarks

Muhammad Umair Hassan; Md Shakil Ahamed Shohag; Dongmei Niu; Kamran Shaukat; Mingxuan Zhang; Wenshuang Zhao; Xiuyang Zhao

doi:10.1117/12.2539640

14 August 2019 A framework for the revision of large-scale image retrieval benchmarks

Muhammad Umair Hassan, Md Shakil Ahamed Shohag, Dongmei Niu, Kamran Shaukat, Mingxuan Zhang, Wenshuang Zhao, Xiuyang Zhao

Author Affiliations +

Proceedings Volume 11179, Eleventh International Conference on Digital Image Processing (ICDIP 2019); 111794D (2019) https://doi.org/10.1117/12.2539640
Event: Eleventh International Conference on Digital Image Processing (ICDIP 2019), 2019, Guangzhou, China

Abstract

Content-based Image Retrieval (CBIR) has been studied over decades and starting from conventional local handcrafted methods to CNN-based methods many works have achieved the best performances in retrieval tasks using query expansion, average query expansion, and query fusion techniques. This work presents a novel approach to revisit the large-scale image retrieval benchmarks Oxford building and Paris building using the SIFT and CNN-based approach. In this paper, we have revised two image retrieval methods and combined the approaches for better performance on image retrieval tasks by describing the annotation errors that have not discussed earlier. The new extensive queries were added for each dataset, making it difficult for the retrieval query phase. VGG-16 network used and RootSIFT applied for feature extraction step whereas T-embedding and democratic aggregation applied on the local descriptors. Query expansion which is an extensive technique for retrieval accuracy is used to check the validation of the proposed pipeline, and our framework achieved the state-of-the-art in addressing the retrieval results compared to other CBIR methods.

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Muhammad Umair Hassan, Md Shakil Ahamed Shohag, Dongmei Niu, Kamran Shaukat, Mingxuan Zhang, Wenshuang Zhao, and Xiuyang Zhao "A framework for the revision of large-scale image retrieval benchmarks", Proc. SPIE 11179, Eleventh International Conference on Digital Image Processing (ICDIP 2019), 111794D (14 August 2019); https://doi.org/10.1117/12.2539640

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KEYWORDS

Image retrieval

Feature extraction

Databases

Computer programming

Convolutional neural networks

Visualization

Diffusion

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