Image annotation with Photocopain

Photo annotation is a resource-intensive task, yet is increasingly essential as image archives and personal photo collections grow in size. There is an inherent conflict in the process of describing and archiving personal experiences, because casual users are generally unwilling to expend large amounts of effort on creating the annotations which are required to organise their collections so that they can make best use of them. This paper describes the Photocopain system, a semi-automatic image annotation system which combines information about the context in which a photograph was captured with information from other readily available sources in order to generate outline annotations for that photograph that the user may further extend or amend

Multimedia information technology and the annotation of video

The state of the art in multimedia information technology has not progressed to the point where a single solution is available to meet all reasonable needs of documentalists and users of video archives. In general, we do not have an optimistic view of the usability of new technology in this domain, but digitization and digital power can be expected to cause a small revolution in the area of video archiving. The volume of data leads to two views of the future: on the pessimistic side, overload of data will cause lack of annotation capacity, and on the optimistic side, there will be enough data from which to learn selected concepts that can be deployed to support automatic annotation. At the threshold of this interesting era, we make an attempt to describe the state of the art in technology. We sample the progress in text, sound, and image processing, as well as in machine learning

Easy on that trigger dad: a study of long term family photo retrieval

We examine the effects of new technologies for digital photography on people's longer term storage and access to collections of personal photos. We report an empirical study of parents' ability to retrieve photos related to salient family events from more than a year ago. Performance was relatively poor with people failing to find almost 40% of pictures. We analyze participants' organizational and access strategies to identify reasons for this poor performance. Possible reasons for retrieval failure include: storing too many pictures, rudimentary organization, use of multiple storage systems, failure to maintain collections and participants' false beliefs about their ability to access photos. We conclude by exploring the technical and theoretical implications of these findings

Easy on that trigger dad: a study of long term family photo retrieval

We examine the effects of new technologies for digital photography on people's longer term storage and access to collections of personal photos. We report an empirical study of parents' ability to retrieve photos related to salient family events from more than a year ago. Performance was relatively poor with people failing to find almost 40% of pictures. We analyze participants' organizational and access strategies to identify reasons for this poor performance. Possible reasons for retrieval failure include: storing too many pictures, rudimentary organization, use of multiple storage systems, failure to maintain collections and participants' false beliefs about their ability to access photos. We conclude by exploring the technical and theoretical implications of these findings

Digital Image Access & Retrieval

The 33th Annual Clinic on Library Applications of Data Processing, held at the University of Illinois at Urbana-Champaign in March of 1996, addressed the theme of "Digital Image Access & Retrieval." The papers from this conference cover a wide range of topics concerning digital imaging technology for visual resource collections. Papers covered three general areas: (1) systems, planning, and implementation; (2) automatic and semi-automatic indexing; and (3) preservation with the bulk of the conference focusing on indexing and retrieval.published or submitted for publicatio

A Study of User's Performance and Satisfaction on the Web Based Photo Annotation with Speech Interaction

This paper reports on empirical evaluation study of users' performance and satisfaction with prototype of Web Based speech photo annotation with speech interaction. Participants involved consist of Johor Bahru citizens from various background. They have completed two parts of annotation task; part A involving PhotoASys; photo annotation system with proposed speech interaction and part B involving Microsoft Microsoft Vista Speech Interaction style. They have completed eight tasks for each part including system login and selection of album and photos. Users' performance was recorded using computer screen recording software. Data were captured on the task completion time and subjective satisfaction. Participants need to complete a questionnaire on the subjective satisfaction when the task was completed. The performance data show the comparison between proposed speech interaction and Microsoft Vista Speech interaction applied in photo annotation system, PhotoASys. On average, the reduction in annotation performance time due to using proposed speech interaction style was 64.72% rather than using speech interaction Microsoft Vista style. Data analysis were showed in different statistical significant in annotation performance and subjective satisfaction for both styles of interaction. These results could be used for the next design in related software which involves personal belonging management.Comment: IEEE Publication Format, https://sites.google.com/site/journalofcomputing

Media aesthetics based multimedia storytelling.

Since the earliest of times, humans have been interested in recording their life experiences, for future reference and for storytelling purposes. This task of recording experiences --i.e., both image and video capture-- has never before in history been as easy as it is today. This is creating a digital information overload that is becoming a great concern for the people that are trying to preserve their life experiences. As high-resolution digital still and video cameras become increasingly pervasive, unprecedented amounts of multimedia, are being downloaded to personal hard drives, and also uploaded to online social networks on a daily basis. The work presented in this dissertation is a contribution in the area of multimedia organization, as well as automatic selection of media for storytelling purposes, which eases the human task of summarizing a collection of images or videos in order to be shared with other people. As opposed to some prior art in this area, we have taken an approach in which neither user generated tags nor comments --that describe the photographs, either in their local or on-line repositories-- are taken into account, and also no user interaction with the algorithms is expected. We take an image analysis approach where both the context images --e.g. images from online social networks to which the image stories are going to be uploaded--, and the collection images --i.e., the collection of images or videos that needs to be summarized into a story--, are analyzed using image processing algorithms. This allows us to extract relevant metadata that can be used in the summarization process. Multimedia-storytellers usually follow three main steps when preparing their stories: first they choose the main story characters, the main events to describe, and finally from these media sub-groups, they choose the media based on their relevance to the story as well as based on their aesthetic value. Therefore, one of the main contributions of our work has been the design of computational models --both regression based, as well as classification based-- that correlate well with human perception of the aesthetic value of images and videos. These computational aesthetics models have been integrated into automatic selection algorithms for multimedia storytelling, which are another important contribution of our work. A human centric approach has been used in all experiments where it was feasible, and also in order to assess the final summarization results, i.e., humans are always the final judges of our algorithms, either by inspecting the aesthetic quality of the media, or by inspecting the final story generated by our algorithms. We are aware that a perfect automatically generated story summary is very hard to obtain, given the many subjective factors that play a role in such a creative process; rather, the presented approach should be seen as a first step in the storytelling creative process which removes some of the ground work that would be tedious and time consuming for the user. Overall, the main contributions of this work can be capitalized in three: (1) new media aesthetics models for both images and videos that correlate with human perception, (2) new scalable multimedia collection structures that ease the process of media summarization, and finally, (3) new media selection algorithms that are optimized for multimedia storytelling purposes.Postprint (published version

Context-aware person identification in personal photo collections

Identifying the people in photos is an important need for users of photo management systems. We present MediAssist, one such system which facilitates browsing, searching and semi-automatic annotation of personal photos, using analysis of both image content and the context in which the photo is captured. This semi-automatic annotation includes annotation of the identity of people in photos. In this paper, we focus on such person annotation, and propose person identification techniques based on a combination of context and content. We propose language modelling and nearest neighbor approaches to context-based person identification, in addition to novel face color and image color content-based features (used alongside face recognition and body patch features). We conduct a comprehensive empirical study of these techniques using the real private photo collections of a number of users, and show that combining context- and content-based analysis improves performance over content or context alone

Organising a daily visual diary using multifeature clustering

The SenseCam is a prototype device from Microsoft that facilitates automatic capture of images of a person's life by integrating a colour camera, storage media and multiple sensors into a small wearable device. However, efficient search methods are required to reduce the user's burden of sifting through the thousands of images that are captured per day. In this paper, we describe experiments using colour spatiogram and block-based cross-correlation image features in conjunction with accelerometer sensor readings to cluster a day's worth of data into meaningful events, allowing the user to quickly browse a day's captured images. Two different low-complexity algorithms are detailed and evaluated for SenseCam image clustering