Accuracy measurement for image retrieval system

Abstract

During the past decades we have been observing a permanent increase in image data, leading to huge repositories. Content-based image retrieval (CBIR) methods have tried to improve the access to image data. To date, numerous feature extraction methods have been proposed to improve the quality of CBIR and image classification systems. In this paper, we are analyzing the technique of relevance feedback for the purpose of image retrieval system. The survey is used to study all the methods used for image retrieval system. The structure-based features its task as broad as texture image retrieval and/or classification. To develop a structure based feature extraction, we have to investigate CBIR and classification problems. Digital image libraries are becoming more common and widely used as visual information is produced at a rapidly growing rate. Creating and storing digital images is nowadays easy and getting more affordable. As a result, the amount of data in visual form is increasing and there is a strong need for effective ways to manage and process it. We have studied support vector machines to learn the feature space distribution of our structure-based features for several images classes. . CBIR contains three levels namely retrieval by primitive features, retrieval by logical features and retrieval by abstract attributes. It contains the problem of finding images relevant to the users’ information needs from image databases, based principally on low-level visual features for which automatic extraction methods are available. Due to the inherently weak connection between the high-level semantic concepts and the low-level visual features the task of developing this kind of systems is very challenging. A popular method to improve image retrieval performance is to shift from single-round queries to navigational queries. This kind of operation is commonly referred to as relevance feedback and can be considered as supervised learning to adjust the subsequent retrieval process by using information gathered from the user’s feedback. Here we also studied an image indexing method based on a Self-Organizing Maps (SOM). The SOM was interpreted as a combination of clustering and dimensionality reduction. It has the advantage of providing a natural ordering for the clusters due to the preserved topology. This way, the relevance information obtained from the user can be spread to neighboring image clusters. The dimensionality reduction aspect of the algorithm alleviates computational requirements of the algorithm. It definitely contains the feature of novel relevance feedback technique. The relevance feedback technique is based on spreading the user responses to local self organizing maps neighborhoods. With some experiments, it will be confirmed that the efficiency of semantic image retrieval can be substantially increased by using these features in parallel with the standard low-level visual features. The measurements like precision and recall were used to evaluate the performance. Precision-recall graph for the 1.000 and 10,000 image data-set and robustness analysis of the 1.000 image database for different brightness have taken.