Towards Searchable Line Drawings, a Content-Based Symbol Retrieval Approach with Variable Query Complexity

Current symbol spotting and retrieval methods are not yet able to achieve the goal of both high accuracy and efficiency on large databases of line drawings. This paper presents an approach for focused symbol retrieval as step towards achieving such a goal by using concepts from image retrieval. During the off-line learning phase of the proposed approach, regions of interest are extracted from the drawings based on feature grouping. The regions are then described using an off-the-shelf descriptor. The similar descriptors are clustered, and finally a visual symbol vocabulary is learned by an SVM classifier. The vocabulary is constructed assuming no knowledge of the contents of the drawings. During on-line retrieval, the classifier recognizes the descriptors of query regions. A query can be a partial or a complete symbol, can contain contextual noise around a symbol or more than one symbol. Experimental results are presented for a database of architectural floor plans.