Efficient Unsupervised Distance Learning through Rank Correlation Measures on Heterogeneous Systems
Resumo
The huge growth of image collections have demanded methods capable of conducting effective and efficient image searches. Among the most promising approaches, the Content-Based Image Retrieval (CBIR) systems have established as an alternative for automatically taking into account the visual information. Despite the important results achieved, retrieving relevant images (effectiveness) in minimal time (efficiency) remains a challenge task. Recently, unsupervised learning algorithms have been proposed to improve the effectiveness of CBIR systems by exploiting similarity and ranking information. Such algorithms does not require any user information, but often demand high computational efforts. On the other hand, parallel and heterogeneous approaches constitute a feasible solution for high performance computing. In this paper, we discuss a parallel and accelerated solution for computing the RL-Sim∗ Algorithm, a recently proposed unsupervised image re-ranking approach. The proposed algorithm uses the OpenCL standard, exploiting both CPU and GPU devives in an Accelerated Processing Unit (APU). The experimental evaluation demonstrated that significant speedups were achieved when compared with the original approach.
Referências
Chatzichristofis, S. A. and Boutalis, Y. S. (2008a). Cedd: color and edge directivity descriptor: a compact descriptor for image indexing and retrieval. In Proceedings of the 6th international conference on Computer vision systems, ICVS’08, pages 312– 322.
Chatzichristofis, S. A. and Boutalis, Y. S. (2008b). Fcth: Fuzzy color and texture histogram - a low level feature for accurate image retrieval. In WIAMIS ’08, pages 191–196.
da S. Torres, R. and Falc˜ao, A. X. (2007). Contour Salience Descriptors for Effective Image Retrieval and Analysis. Image and Vision Computing, 25(1):3–13.
Datta, R., Joshi, D., Li, J., and Wang, J. Z. (2008). Image retrieval: Ideas, influences, and trends of the new age. ACM Computing Surveys, 40(2):5:1–5:60.
Fagin, R., Kumar, R., and Sivakumar, D. (2003). Comparing top k lists. In ACM-SIAM Symposium on Discrete algorithms (SODA’03), pages 28–36.
Gopalan, R., Turaga, P., and Chellappa, R. (2010). Articulation-invariant representation of non-planar shapes. In ECCV’2010, volume 3, pages 286–299.
Huang, J., Kumar, S. R., Mitra, M., Zhu, W.-J., and Zabih, R. (1997). Image indexing using color correlograms. In CVPR, pages 762–768.
Latecki, L. J., Lakmper, R., and Eckhardt, U. (2000). Shape descriptors for non-rigid shapes with a single closed contour. In CVPR, pages 424–429.
Ling, H. and Jacobs, D. W. (2007). Shape classification using the inner-distance. PAMI, 29(2):286–299.
Ling, H., Yang, X., and Latecki, L. J. (2010). Balancing deformability and discriminability for shape matching. In ECCV, volume 3, pages 411–424.
Lowe, D. (1999). Object recognition from local scale-invariant features. In ICCV, pages 1150–1157.
Nistér, D. and Stewénius, H. (2006). Scalable recognition with a vocabulary tree. In CVPR, volume 2, pages 2161–2168.
Okada, C. Y., Pedronette, D. C. G., and Torres, R. d. S. (2015). Unsupervised distance learning by rank correlation measures for image retrieval. In ICMR ’15.
Pedronette, D. C. G. and da S. Torres, R. (2010). Shape retrieval using contour features and distance optmization. In VISAPP, volume 1, pages 197 – 202.
Pedronette, D. C. G. and da S. Torres, R. (2013). Image re-ranking and rank aggregation based on similarity of ranked lists. Pattern Recognition, 46(8):2350–2360.
Pedronette, D. C. G., da S. Torres, R., Borin, E., and Breternitz, M. (2013). Image Reranking Acceleration on GPUs. In SBAC-PAD.
Stehling, R. O., Nascimento, M. A., and Falc˜ao, A. X. (2002). A compact and efficient image retrieval approach based on border/interior pixel classification. In CIKM, pages 102–109.
Stone, J. E., Gohara, D., and Shi, G. (2010). OpenCL: A parallel programming standard for heterogeneous computing systems. Computing in Science Engineering, 12(3):66 –73.
Swain, M. J. and Ballard, D. H. (1991). Color indexing. International Journal on Computer Vision, 7(1):11–32.
van de Weijer, J. and Schmid, C. (2006). Coloring local feature extraction. In ECCV, pages 334–348.
Yang, X., Koknar-Tezel, S., and Latecki, L. J. (2009). Locally constrained diffusion process on locally densified distance spaces with applications to shape retrieval. In CVPR, pages 357–364.
Zagoris, K., Chatzichristofis, S., Papamarkos, N., and Boutalis, Y. (2010). Automatic image annotation and retrieval using the joint composite descriptor. In 14th Panhellenic Conference on Informatics (PCI), pages 143–147.
