Object Centric Image Retrieval for Personal Photo Collections - Printable Version +- Free Academic Seminars And Projects Reports (https://easyreport.in) +-- Forum: Project Ideas And Disscussion (https://easyreport.in/forumdisplay.php?fid=32) +--- Forum: Engineering Project Ideas (https://easyreport.in/forumdisplay.php?fid=33) +---- Forum: Computer Science Project Ideas (https://easyreport.in/forumdisplay.php?fid=36) +---- Thread: Object Centric Image Retrieval for Personal Photo Collections (/showthread.php?tid=6140) |
Object Centric Image Retrieval for Personal Photo Collections - sheny - 08-16-2017 [attachment=15220] [attachment=15221] [attachment=15222] [attachment=15223] 1. INTRODUCTION There are number of existing retrieval techniques for images. Text based techniques are the most commonly used. In text-based systems the images are first annotated by text and then text indexing and retrieval techniques are used. Content based retrieval techniques followed the text-based system where in low level image features like color, texture etc. are used to retrieve similar images. In text-based systems, manual annotation poses a difficulty and in visual-based systems, subjectivity of human perception makes it a tough task. The low level features fail to capture the high level semantics of the image in content based systems. To help capture the high level semantics, human is looped in and the process of relevance feedback is used to make the system adapt to the user. Retrieval techniques used for search try to find the most similar images given to user input in the form of text, visual or other features. But in case the aim of user is to explore the set of images, s/he might not know the description of the image. Returning the most similar images as results of the initial query might not be best idea. Catering to the needs of users who want to explore rather than search is one of the human centered research areas in multimedia. In this project, a Content-based Image Retrieval system for finding similar images in personalized photo collections on local machine is described. Instead of matching low level features, the attempt is to match the objects in various images. The image is treated as the sum of objects of interest and remaining background. User can register important regions in images which in essence are the objects of interest. In a personalized collection, the objects of interest can be broadly classified into - people and places. The SIFT features have been found to be useful in recognizing faces and is by definition scale and rotation invariant hence making it suitable to detect rigid objects in the background. Hence for every image in the database, the best match for all registered regions is stored and the MPEG 7 color and texture features are stored for the remaining background. MPEG 7 features have been extensively used in practice for content based multimedia retrieval. The flowchart of the system, description of the components and the details of the algorithms to be used are discussed in this report. 2. MOTIVATION In a diversity of areas ranging from medical, astronomy, geology, military and so on, the applications of imaging techniques have been ever increasing. Image acquisition, storing, transfer and retrieval techniques have undergone tremendous improvement over the past few decades. The common property underlying the aim of evaluating images irrespective of the field of interest is the need to explore the image and infer valuable information from it. Thus, the semantics of the image are far more important than the features of image which brings us to biggest challenge in image retrieval and exploration applications bridging the semantic gap. It refers to the intelligent use of low level features and well defined metadata to understand the semantics of the image. Since an image usually intends to capture an object in particular, for example, brain scans in MR imaging, satellite imagery of area under military surveillance or the simply the personal photographs of any person or place. |