|Tiêu đề||Spotting Symbol using Sparsity over Learned Dictionary of Local Descriptors|
|Loại công bố||Conference Paper|
|Năm xuất bản||2014|
|Tác giả||Do, T-H, Tabbone, S, Ramos-Terrades, O|
|Conference Name||11th International Workshop on Document Analysis Systems|
|Conference Location||Tours, France|
|Tóm tắt|| |
This paper proposes a new approach to spot symbols into graphical documents using sparse representations. More specifically, a dictionary is learned from a training database of local descriptors defined over the documents. Following their sparse representations, interest points sharing similar properties are used to define interest regions. Using an original adaptation of information retrieval techniques, a vector model for interest regions and for a query symbol is built based on its sparsity in a visual vocabulary where the visual words are columns in the learned dictionary. The matching process is performed comparing the similarity between vector models. Evaluation on SESYD datasets demonstrates that our method is promising.