Solving Partial Assignment Problems using Random Clique Complexes
Authors: Charu Sharma, Deepak Nathani, Manohar Kaul
ICML 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we present a comprehensive empirical study that compares our method s matching accuracy to that of a diverse set of matching approaches (Zhou & De la Torre, 2016; Zhou & De la Torre, 2013; Cho et al., 2010; Feizi et al., 2016; Leordeanu & Hebert, 2005; Cour et al., 2007; Pachauri et al., 2013; Gold & Rangarajan, 1996; Kuhn, 1955; Leordeanu et al., 2009; Zass & Shashua, 2008; Li et al., 2013; Duchenne et al., 2011). We conducted our experiments on both synthetic and well-known hard real-world datasets that span across affine/non-affine transformations, severe occlusions, and clutter. Our study reveals much better accuracy for the popular datasets against several of the state-of-the-art matching methods. |
| Researcher Affiliation | Academia | 1Department of Computer Science & Engineering, Indian Institute of Technology Hyderabad, Hyderabad, India. |
| Pseudocode | Yes | Algorithm 1 Matching Random Clique Complexes Input: X(G) = {G(k,l)}h k=0 and X(G ) = {G (k,l)}h k=0 1: for k = h . . . 0 do 2: Let M, M be the total number of (k + 1)-cliques in G(k,l) and G (k,l), respectively 3: L := {c(k) i }M 1 i=0 # list of barycenters 4: for i = 0 . . . M 1 do 5: Ni := Ni n g(k,l) (x,:) | x = i, g(k,l) (x,y) = 0 o 6: N := N {Ni} # clique neighborhoods 7: end for 8: for i = 0 . . . M 1 do 9: αi := [α1, . . . , αM 1]T 10: α := α {αi} # affine weight vectors 11: end for 12: Repeat steps 3 11 on G (k,l) for L , N and α . 13: Build cost matrix C(k) from weights vectors α, α 14: X k := Kuhn-Munkres (G(k,l), G (k,l), C(k)) 15: end for Return: {X 0, . . . , X h} # set of permutation matrices |
| Open Source Code | No | No explicit statement or link indicating that the source code for *this paper's* methodology is publicly available. |
| Open Datasets | Yes | We took two real-world datasets, i.e., Books and Building (Pachauri et al., 2013) |
| Dataset Splits | Yes | we uniformly sample frames (at 20% and 40%) and perform affine transformations on the selected frames to distort them. and we omit 2, 4, 6, 8, and 10 (6.66%, 13.33%, 20%, 26.66%, and 33.33%) points out of total House landmark points (i.e., 30 points) from 40% (Figure 4) of frame sequences randomly. |
| Hardware Specification | No | No specific hardware details (such as GPU/CPU models, memory, or cloud resources) were mentioned for running experiments. |
| Software Dependencies | No | No specific ancillary software dependencies (e.g., library or solver names with version numbers) are mentioned in the paper. |
| Experiment Setup | Yes | We set p = 0.7 and k = 7 as nearest neighbors to get the correct matchings. |