Interactive learning of heterogeneous visual concepts with local features

In the context of computer-assisted plant identification we are facing challenging information retrieval problems because of the very high within-class variability and of the limited number of training examples. To address these problems, we suggest a new interactive learning approach that combines similarity-based retrieval and re-ranking by SVM using local feature distributions. This approach leads to improved sample selection, allowing to obtain better results. Categories and Subject Descriptors: H.3.3 [Information Systems]: Multimedia Information Search and Retrieval; H.3.1 [Information Storage and Retrieval]: Content Analysis and Indexing methods. General Terms: Algorithms.

• Type: Journal
• Year: 2010
• Where: MM
• Authors: Wajih Ouertani, Michel Crucianu, Nozha Boujemaa
• Keywords: Computer-assisted Plant Identification, Information Retrieval Problems, Interactive Learning Approach, MM 2010, Multimedia