Locality Preserving Markovian Transition for Instance Retrieval

Jifei Luo, Wenzheng Wu, Hantao Yao, Lu Yu, Changsheng Xu
Proceedings of the 42nd International Conference on Machine Learning, PMLR 267:41407-41431, 2025.

Abstract

Diffusion-based re-ranking methods are effective in modeling the data manifolds through similarity propagation in affinity graphs. However, positive signals tend to diminish over several steps away from the source, reducing discriminative power beyond local regions. To address this issue, we introduce the Locality Preserving Markovian Transition (LPMT) framework, which employs a long-term thermodynamic transition process with multiple states for accurate manifold distance measurement. The proposed LPMT first integrates diffusion processes across separate graphs using Bidirectional Collaborative Diffusion (BCD) to establish strong similarity relationships. Afterwards, Locality State Embedding (LSE) encodes each instance into a distribution for enhanced local consistency. These distributions are interconnected via the Thermodynamic Markovian Transition (TMT) process, enabling efficient global retrieval while maintaining local effectiveness. Experimental results across diverse tasks confirm the effectiveness of LPMT for instance retrieval.

Cite this Paper

BibTeX
@InProceedings{pmlr-v267-luo25n, title = {Locality Preserving {M}arkovian Transition for Instance Retrieval}, author = {Luo, Jifei and Wu, Wenzheng and Yao, Hantao and Yu, Lu and Xu, Changsheng}, booktitle = {Proceedings of the 42nd International Conference on Machine Learning}, pages = {41407--41431}, year = {2025}, editor = {Singh, Aarti and Fazel, Maryam and Hsu, Daniel and Lacoste-Julien, Simon and Berkenkamp, Felix and Maharaj, Tegan and Wagstaff, Kiri and Zhu, Jerry}, volume = {267}, series = {Proceedings of Machine Learning Research}, month = {13--19 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v267/main/assets/luo25n/luo25n.pdf}, url = {https://proceedings.mlr.press/v267/luo25n.html}, abstract = {Diffusion-based re-ranking methods are effective in modeling the data manifolds through similarity propagation in affinity graphs. However, positive signals tend to diminish over several steps away from the source, reducing discriminative power beyond local regions. To address this issue, we introduce the Locality Preserving Markovian Transition (LPMT) framework, which employs a long-term thermodynamic transition process with multiple states for accurate manifold distance measurement. The proposed LPMT first integrates diffusion processes across separate graphs using Bidirectional Collaborative Diffusion (BCD) to establish strong similarity relationships. Afterwards, Locality State Embedding (LSE) encodes each instance into a distribution for enhanced local consistency. These distributions are interconnected via the Thermodynamic Markovian Transition (TMT) process, enabling efficient global retrieval while maintaining local effectiveness. Experimental results across diverse tasks confirm the effectiveness of LPMT for instance retrieval.} }
Endnote
%0 Conference Paper %T Locality Preserving Markovian Transition for Instance Retrieval %A Jifei Luo %A Wenzheng Wu %A Hantao Yao %A Lu Yu %A Changsheng Xu %B Proceedings of the 42nd International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2025 %E Aarti Singh %E Maryam Fazel %E Daniel Hsu %E Simon Lacoste-Julien %E Felix Berkenkamp %E Tegan Maharaj %E Kiri Wagstaff %E Jerry Zhu %F pmlr-v267-luo25n %I PMLR %P 41407--41431 %U https://proceedings.mlr.press/v267/luo25n.html %V 267 %X Diffusion-based re-ranking methods are effective in modeling the data manifolds through similarity propagation in affinity graphs. However, positive signals tend to diminish over several steps away from the source, reducing discriminative power beyond local regions. To address this issue, we introduce the Locality Preserving Markovian Transition (LPMT) framework, which employs a long-term thermodynamic transition process with multiple states for accurate manifold distance measurement. The proposed LPMT first integrates diffusion processes across separate graphs using Bidirectional Collaborative Diffusion (BCD) to establish strong similarity relationships. Afterwards, Locality State Embedding (LSE) encodes each instance into a distribution for enhanced local consistency. These distributions are interconnected via the Thermodynamic Markovian Transition (TMT) process, enabling efficient global retrieval while maintaining local effectiveness. Experimental results across diverse tasks confirm the effectiveness of LPMT for instance retrieval.
APA
Luo, J., Wu, W., Yao, H., Yu, L. & Xu, C.. (2025). Locality Preserving Markovian Transition for Instance Retrieval. Proceedings of the 42nd International Conference on Machine Learning, in Proceedings of Machine Learning Research 267:41407-41431 Available from https://proceedings.mlr.press/v267/luo25n.html.

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