A Novel Feature Matching Strategy for Large Scale Image Retrieval
Authors: Hao Tang, Hong Liu
IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The lr SA strategy is evaluated through extensive experiments on five benchmark datasets. Experiments show that the results exceed the retrieval performance of current quantization methods on these datasets. Combined with postprocessing steps, we have achieved competitive results compared with the state-of-the-art methods. |
| Researcher Affiliation | Academia | Hao Tang and Hong Liu Engineering Lab on Intelligent Perception for Internet of Things (ELIP) Key Laboratory of Machine Perception (Ministry of Education) Shenzhen Graduate School, Peking University, Beijing 100871, China haotang@sz.pku.edu.cn, hongliu@pku.edu.cn |
| Pseudocode | Yes | Algorithm 1 Image retrieval pipeline using lr SA strategy. |
| Open Source Code | No | The paper does not provide any explicit statement or link indicating the release of source code for the described methodology. |
| Open Datasets | Yes | To evaluate the effectiveness of the proposed lr SA strategy and image retrieval pipeline, we have conducted experiments on five publicly available datasets: Ukbench [Nister and others, 2006], Oxford 5K [Philbin et al., 2007], Paris 6K [Philbin et al., 2008], Holidays [J egou et al., 2008] and MIR Flickr 1M [Huiskes et al., 2010]. |
| Dataset Splits | No | The paper mentions the use of datasets for evaluation and codebook construction (Flickr60k), but it does not specify explicit train/validation/test splits (e.g., percentages or counts) for the main experiments performed on the benchmark datasets (Oxford, Paris, Holidays, Ukbench). |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., GPU/CPU models, memory) used to conduct the experiments. |
| Software Dependencies | No | The paper mentions using 'the FLANN library [Muja and Lowe, 2014] to perform Approximate Nearest Neighbors (ANN) computations' but does not specify a version number for FLANN or any other software dependencies. |
| Experiment Setup | Yes | Five parameters are involved in the proposed lr SA strategy: the weight σ, multiple assignment i and T, threshold value t1 and t2. We set i and T the same as [J egou et al., 2010] to 10 and 4, respectively. σ is set to 6250 similar to that in [Philbin et al., 2008]. t1 and t2 are set as 0.6 and 0.4 according to the observation of experimental results, respectively. Two parameters are involved in Hamming Embedding: the Hamming distance threshold and weighting factor β. We set and β to 4 and 7, respectively, the same as those in [Zheng et al., 2014a]. |