TY - JOUR AU - Pau Riba AU - Josep Llados AU - Alicia Fornes PY - 2020// TI - Hierarchical graphs for coarse-to-fine error tolerant matching T2 - PRL JO - Pattern Recognition Letters SP - 116 EP - 124 VL - 134 KW - Hierarchical graph representation KW - Coarse-to-fine graph matching KW - Graph-based retrieval N2 - During the last years, graph-based representations are experiencing a growing usage in visual recognition and retrieval due to their ability to capture both structural and appearance-based information. Thus, they provide a greater representational power than classical statistical frameworks. However, graph-based representations leads to high computational complexities usually dealt by graph embeddings or approximated matching techniques. Despite their representational power, they are very sensitive to noise and small variations of the input image. With the aim to cope with the time complexity and the variability present in the generated graphs, in this paper we propose to construct a novel hierarchical graph representation. Graph clustering techniques adapted from social media analysis have been used in order to contract a graph at different abstraction levels while keeping information about the topology. Abstract nodes attributes summarise information about the contracted graph partition. For the proposed representations, a coarse-to-fine matching technique is defined. Hence, small graphs are used as a filtering before more accurate matching methods are applied. This approach has been validated in real scenarios such as classification of colour images or retrieval of handwritten words (i.e. word spotting). UR - https://doi.org/10.1016/j.patrec.2019.02.001 N1 - DAG; 600.097; 601.302; 603.057; 600.140; 600.121 ID - Pau Riba2020 ER -