Implementation of MIML Framework using Annotated Image Dataset

As MIL (Multi-Instance Learning) considers only input ambiguity and MLL (Multi-Label Learning) consider only output ambiguity, we require a framework which consider both ambiguities together and solve the complex problems. MIML (Multi-Instance Multi-Label) framework can solve this problem, but the implementation of MIML dataset is more complex as it considers multiple labels and its multiple instances both together. This research work focuses on implementation of MIML framework using 2014 annotated natural scene image dataset. An image annotation task is closely related to MIML learning problem. Multi class SVM (MSVMpack) used to handle classification of more than two classes without depending on different decomposition methods. Bag of Regions (BoR) is used as a bag generator which is well known framework to generate local features from images. SIFT Scale-Invariant Feature Transform (SIFT) good descriptor can handle intensity, rotation and scale with variations. During experiment for each image SIFT descriptors are extracted for each shot. As a result it also provide vector of predicted labels, accuracy rate during classification, hamming loss, one-error, coverage and R-loss after testing the model. KeywordsMIML Classification Framework, Image Annotation, Multi class SVM, SIFT, BoR.

[1]  Anita Shinde,et al.  A Study on Image Annotation Techniques , 2012 .

[2]  Zhi-Hua Zhou,et al.  Multilabel Neural Networks with Applications to Functional Genomics and Text Categorization , 2006, IEEE Transactions on Knowledge and Data Engineering.

[3]  Tomás Lozano-Pérez,et al.  A Framework for Multiple-Instance Learning , 1997, NIPS.

[4]  Zhi-Hua Zhou,et al.  M3MIML: A Maximum Margin Method for Multi-instance Multi-label Learning , 2008, 2008 Eighth IEEE International Conference on Data Mining.

[5]  Min-Ling Zhang,et al.  MIMLRBF: RBF neural networks for multi-instance multi-label learning , 2009, Neurocomputing.

[6]  Jason Weston,et al.  Support vector machines for multi-class pattern recognition , 1999, ESANN.

[7]  David G. Lowe,et al.  Object recognition from local scale-invariant features , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[8]  Zhi-Hua Zhou,et al.  Multi-Instance Multi-Label Learning with Application to Scene Classification , 2006, NIPS.

[9]  Grigorios Tsoumakas,et al.  Mining Multi-label Data , 2010, Data Mining and Knowledge Discovery Handbook.

[10]  Jenny Benois-Pineau,et al.  Content Based Image Retrieval Using Bag-Of-Regions , 2012, MMM.

[11]  Vladimir Pavlovic,et al.  A New Baseline for Image Annotation , 2008, ECCV.

[12]  Mohammad S. Sorower.  A Literature Survey on Algorithms for Multi-label Learning , 2010 .

[13]  A. Vedaldi.  An open implementation of the SIFT detector and descriptor , 2007 .

[14]  Zhi-Hua Zhou,et al.  Multi-instance multi-label learning , 2008, Artif. Intell..

[15]  Jun Yang.  Review of Multi-Instance Learning and Its applications , 2005 .