Bonfring International Journal of Advances in Image Processing, Vol. 1, Special Issue, December 2011 1 ISSN 2250 – 1053 | © 2011 Bonfring Abstract--- In this paper, we propose the MIML (Multi- Instance Multi-Label learning) framework which is associated with multiple class labels for Image Annotation. Compared to traditional learning frameworks, the MIML framework is more convenient and natural for representing complicated objects which have multiple semantic meanings. To learn from MIML examples we have taken a survey on MIML Boost, MIMLSVM, D-MIMLSVM, InsDif and SubCod algorithms. MIML Boost and MIML SVM are based on a simple degeneration strategy. Experiments based on this algorithm shows that solving problems involving complicated objects with multiple semantic meanings in the MIML framework can lead to good performance. As the degeneration process may lose information, we have considered D-MimlSvm algorithm which tackles MIML problems directly in a regularization framework. InsDif and SubCod algorithms works by transforming single-instances into the MIML representation for learning, while SubCod works by transforming single-label examples into the MIML representation for learning. We have compared the results of all the algorithms and have identified that InsDif framework leads to good performance rates. Keywords--- Machine Learning, Multi-Instance Multi- Label Learning, Multi-Label Learning, Multi-Instance Learning I. INTRODUCTION N traditional supervised learning, an object is represented by an instance, i.e., a feature vector, and associated with a class label. Formally, let X denote the instance space (or feature space) and Y the set of class labels. The task is to learn a function f : X → Y from a given data set {(x1, y1), (x2, y2), …., (xm, ym)}, where x i X is an instance and y i Y is the known label of x i . Although this formalization is prevailing and successful, there are many real-world problems which do not fit in this framework. Each object in this framework belongs to only one concept and therefore the corresponding instance is associated with a single class label. However, many real-world objects are complicated, which may belong to T. Sumathi, Department of Software Systems, Karpagam University, Coimbatore-21. E-mail: tsumathijk@gmail.com C. Lakshmi Devasena, Department of Software Systems, Karpagam University, Coimbatore-21. E-mail: devaradhe2007@gmail.com R. Revathi, Department of Software Systems, Karpagam University, Coimbatore-21 S. Priya, Department of Software Systems, Karpagam University, Coimbatore-21 Dr.M. Hemalatha, Department of Software Systems, Karpagam University, Coimbatore-21. E-mail: hema.bioinf@gmail.com multiple concepts simultaneously. To choose the right semantic meaning for such objects for a specific scenario is the fundamental difficulty of many tasks. In contrast to starting from a large universe of all possible concepts involved in the task, it may be helpful to get the subset of concepts associated with the concerned object at first, and then make a choice in the small subset later. However, getting the subset of concepts, ie. assigning proper class labels to such objects is still a challenging task. We notice that as an alternative to representing an object by a single instance, in many cases it is possible to represent a complicated object using a set of instances. For example, multiple patches can be extracted from an image where each patch is described by an instance, and thus the image can be represented by a set of instances. Using multiple instances to represent those complicated objects may be helpful because some inherent patterns which are closely related to some labels may become explicit and clearer. In this paper, we propose the MIML (Multi-Instance Multi-Label learning) framework, where an example is explained by multiple instances and correlated with multiple class labels. Compared to traditional learning frameworks, the MIML framework is more convenient and natural for representing complicated objects. To exploit the advantages of the MIML representation, new learning algorithms are needed. We have compared the MIMLBoost algorithm and the MIMLSVM algorithm based on a simple degeneration strategy, and experiments show that solving problems involving complicated objects with multiple semantic meanings under the MIML framework can lead to good performance. Considering that the degeneration process may lose information, we have compared the performance of D- MIMLSVM (i.e., Direct MIMLSVM) algorithm which tackles MIML problems directly in a regularization framework. In some practical tasks we do not have access to the real objects themselves such as the real images; instead, we are given observational data where each real object has already been represented by a single instance. Thus, in such cases we cannot capture more information from the real objects using the MIML representation. Even in this situation, however, MIML is still useful. We have also compared the performance of InsDif (i.e., INStance DIFferentiation) algorithm which transforms Single-instances into MIML examples for learning. MIML can also be helpful for learning single-label objects. For which we have compared the SubCod (i.e., SUB-COncept Discovery) algorithm which works by discovering sub- concepts of the target concept at first and then transforming Automatic Image Annotation and Retrieval using Multi-Instance Multi-Label Learning T. Sumathi, C. Lakshmi Devasena, R. Revathi, S. Priya and Dr.M. Hemalatha I