“Analysis Of Various Cbir Techniques For Retriving Forensically Viable Information From The Images” Vijay Bagdi* MTech (CSE) Student, TGPCET, Nagpur, Maharashtra, India Sulabha Patil Professor, Post Graduate Dept of CSE, TGPCET, Nagpur, Maharashtra, India R.V. Dharaskar Director, MPGI, Nanded, Maharashtra, India Abstract - Image retrieval has been one of the most interesting and vivid research areas in the field of computer vision .When the images are geo-tagged the information contained in them constitute an important factor in digital forensics. There are various techniques through which the information stored in an image can be retrieved for forensic evidences. Content-based image retrieval (CBIR) systems are used in order to automatically index, search, retrieve and browse image databases. Color and texture features are important properties in content-based image retrieval systems. In this paper we have mentioned detailed analysis of CBIR system. Keywords - CBIR, TBIR, Image Retrieval, Feature Extraction I. INTRODUCTION Digital forensic is playing an important role considering the availability of the storage media such as Clouds, increase in the use of social networking sites wherein people are uploading their personal information. Use of mobile phones, smart phones for uploading geo- tagged images is also increased. These images constitute as an important evidence for forensics. Human judge similarity of image and sounds according to their semantic contents, for instance the searching for a politician‟s picture is based on his facial characters or other contents. Content-based image retrieval (CBIR) is a technique for retrieving images on the basis of automatically derived features such as color, texture and shape. Users in many professional fields are exploiting the opportunities offered by the ability to access and manipulate remotely-stored images in all kinds of new and exciting ways [1] [2] After a decade of intensive research, CBIR technology is now beginning to move out of the laboratory and into the marketplace, in the form of commercial products like QBIC [3] and Virage [4]. The history of the content-based image retrieval can be divided into three phases: - The retrieval based on artificial notes. - The retrieval based on vision character of image contents. - The retrieval based on image semantic features. The Image Retrieval based on artificial notes using traditional keywords having two problems. First it brings heavy workload and second it still remains uncertainty and subjectivity. As image retrieval based on artificial notes still remains insufficiency, the image feature extraction has been come up. The accuracy of image is depends on the extracted features. So the research based on feature extraction is now focused. The feature of vision can be classified by semantic hierarchy into middle level feature and low- level feature. Low- level feature includes color, texture and inflexion. Middle level involves shape description and object feature [5, 6, 7, 8, 9]. Fig 1: Text Based Image Retrieval (TBIR) [5] A. Text-Based Image Retrieval The TBIR technique is easy to implement as input is text only. The retrieval of images is very fast and useful for searching web images (surrounding text). As shown in fig 1, the input is text which gives collection of images as output. On the other hand it has several disadvantages – – Manual annotation is not always available – Manual annotation is impossible for a large DB – Manual annotation is not accurate – A picture is worth a thousand words – Surrounding text may not describe the image International Journal of Engineering Research & Technology (IJERT) Vol. 2 Issue 1, January- 2013 ISSN: 2278-0181 1 www.ijert.org