TRECVID 2008 - goals, tasks, data, evaluation mechanisms and metrics

The TREC Video Retrieval Evaluation (TRECVID) 2008 is a TREC-style video analysis and retrieval evaluation, the goal of which remains to promote progress in content-based exploitation of digital video via open, metrics-based evaluation. Over the last 7 years this effort has yielded a better understanding of how systems can effectively accomplish such processing and how one can reliably benchmark their performance. In 2008, 77 teams (see Table 1) from various research organizations --- 24 from Asia, 39 from Europe, 13 from North America, and 1 from Australia --- participated in one or more of five tasks: high-level feature extraction, search (fully automatic, manually assisted, or interactive), pre-production video (rushes) summarization, copy detection, or surveillance event detection. The copy detection and surveillance event detection tasks are being run for the first time in TRECVID. This paper presents an overview of TRECVid in 2008

TRECVID 2009 - goals, tasks, data, evaluation mechanisms and metrics

The TREC Video Retrieval Evaluation (TRECVID) 2009 was a TREC-style video analysis and retrieval evaluation, the goal of which was to promote progress in content-based exploitation of digital video via open, metrics-based evaluation. Over the last 9 years TRECVID has yielded a better understanding of how systems can effectively accomplish such processing and how one can reliably benchmark their performance. 63 teams from various research organizations — 28 from Europe, 24 from Asia, 10 from North America, and 1 from Africa — completed one or more of four tasks: high-level feature extraction, search (fully automatic, manually assisted, or interactive), copy detection, or surveillance event detection. This paper gives an overview of the tasks, data used, evaluation mechanisms and performanc

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

Towards precise outdoor localisation based on image recognition

In recent years significant progress has been made in the field of visual search, mostly due to the introduction of powerful local image features. At the same time, a rapid development of mobile platforms enabled deployment of image retrieval systems on mobile devices. Various mobile applications of the visual search have been proposed, one of the most interesting being geo-localization service based on image recognition and other positioning information. This thesis attempts to advance the existing visual search system developed at Telefonica I+D Barcelona so it could be used for high precision geo-localization. In order to do so, this dissertation tackles two significant challenges of image retrieval: improvement in robustness of the detection of similarities between images and increase of discriminatory power. The work advances the state-of-the-art with three main contributions. The first contribution consists of the development of an evaluation framework for visual search engine. Since the assessment of any complex system is crucial for its development and analysis, an exhaustive set of evaluation measures is selected from the relevant literature and implemented. Furthermore, several datasets along with the corresponding information about correct correspondences between the images have been gathered and unified. The second contribution considers the representation of image features describing salient regions and attempts to alleviate the quantization effects introduced during its creation. A mechanism that in the literature is commonly referred to as soft assignment is adapted to a visual search engine of Telefonica I+D with several extensions. The third and final contribution consists of a post-processing stage that increases discriminative power by verification of local correspondences' spatial layout. The performance and generality of the proposed solutions has been analyzed based on extensive evaluation using the framework proposed in this work