A Survey on Feature Analysis and Classification for Image Annotation using Saliency Map

With the advances in multimedia technologies collections of digital images is growing rapidly. Due to the popularity of various digital cameras and the rapid growth of social media tools, internet based photo sharing have increase in daily life. As the database have huge amount of images and other files, it is difficult to retrieve the required images. Supervised dictionary learning feature based image retrieval is very important area of research in the field of image retrieval. The feature based image annotation paradigm aims to tackle the automated image annotation by exploiting feature based image retrieval. Aim of this research work is to extract features related to the image in the form of annotation and develop system for clustering of data user define clusters with the help of image processing. To solve the problem of data classification into large dataset, to get an efficient system which classify data not only on basis of the dataset but also on basis of the image specified class

Lightweight Adaptation of Classifiers to Users and Contexts: Trends of the Emerging Domain

Intelligent computer applications need to adapt their behaviour to contexts and users, but conventional classifier adaptation methods require long data collection and/or training times. Therefore classifier adaptation is often performed as follows: at design time application developers define typical usage contexts and provide reasoning models for each of these contexts, and then at runtime an appropriate model is selected from available ones. Typically, definition of usage contexts and reasoning models heavily relies on domain knowledge. However, in practice many applications are used in so diverse situations that no developer can predict them all and collect for each situation adequate training and test databases. Such applications have to adapt to a new user or unknown context at runtime just from interaction with the user, preferably in fairly lightweight ways, that is, requiring limited user effort to collect training data and limited time of performing the adaptation. This paper analyses adaptation trends in several emerging domains and outlines promising ideas, proposed for making multimodal classifiers user-specific and context-specific without significant user efforts, detailed domain knowledge, and/or complete retraining of the classifiers. Based on this analysis, this paper identifies important application characteristics and presents guidelines to consider these characteristics in adaptation design