Online Hashing with Efficient Updating of Binary Codes
Authors: Zhenyu Weng, Yuesheng Zhu12354-12361
AAAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The experiments on two multi-label image databases demonstrate the effectiveness and the efficiency of our method for multi-label image retrieval. |
| Researcher Affiliation | Academia | Zhenyu Weng, Yuesheng Zhu Communication and Information Security Laboratory, Shenzhen Graduate School, Peking University {wzytumbler, zhuys}@pku.edu.cn |
| Pseudocode | Yes | Algorithm 1 OHWEU Input: streaming data (xi, yi), C, K Output: P and R 1: Learn {wk, bk}K k=1 by PCA-ITQ in the initial stage 2: Initialize P0 = [p0 1, ..., p0 K] and R0 = [r0 1, ..., r0 K] 3: for i = 1, 2, ... do 4: Obtain the binary code g i according to Eqn.(5) 5: for j = 1 to K do 6: Update pi j according to Eqn. (13) 7: Update ri j according to Eqn. (28) 8: end for 9: end for |
| Open Source Code | No | The paper does not provide any statement about making its source code publicly available or a link to a code repository. |
| Open Datasets | Yes | Datasets We compare our method on two commomly-used multilabel image datasets, MS-COCO and NUS-WIDE. (a).MS-COCO dataset (Lin et al. 2014). ... (b). NUS-WIDE dataset (Chua et al. ). |
| Dataset Splits | Yes | (a).MS-COCO dataset (Lin et al. 2014). ... We randomly take 4000 images as the queries, and the rest for training and searching. ... (b). NUS-WIDE dataset (Chua et al. ). ... We randomly take 2000 images as the queries, and the rest for training and searching. |
| Hardware Specification | Yes | The experiments are run on a computer with CPU I7, 24GB memory. ... The experiments are run on a PC with Intel i7 3.4 GHz CPU, 24 GB memory. |
| Software Dependencies | No | The paper does not provide specific version numbers for any software dependencies or libraries used in the experiments. |
| Experiment Setup | Yes | As OKH does, we have m = 300 data points to train the hash functions in the initial stage. ... Based on the results, we set C = 0.1 for MS-COCO and NUS-WIDE. |