ImageCLEF 2013: The Vision, the Data and the Open Challenges

Abstract

Abstract. This paper presents an overview of the ImageCLEF 2013 lab. Since its first edition in 2003, ImageCLEF has become one of the key initiatives promoting the benchmark evaluation of algorithms for the cross-language annotation and retrieval of images in various domains, such as public and personal images, to data acquired by mobile robot platforms and botanic collections. Over the years, by providing new data collections and challenging tasks to the community of interest, the ImageCLEF lab has achieved an unique position in the multi lingual image annotation and retrieval research landscape. The 2013 edition consisted of three tasks: the photo annotation and retrieval task, the plant identification task and the robot vision task. Furthermore, the medical annotation task, that traditionally has been under the ImageCLEF umbrella and that this year celebrates its tenth anniversary, has been organized in conjunction with AMIA for the first time. The paper describes the tasks and the 2013 competition, giving an unifying perspective of the present activities of the lab while discussion the future challenges and opportunities.

Point-based medialness for 2D shape description and identification

“…At this stage in our research program we have performed an extensive experiment on different heterogeneous databases containing biological forms (large animals, plants and insects) to verify the performance in efficiency and accuracy of our novel proposed method. 11 Four main types of datasets were used for this purpose: (i) animals taking a static posture and in movement, (ii) humans taking a static posture or in action (articulated movement), (iii) insects and (iv) plants (only leaf forms). Also, we performed some random re-scaling, rotating and translating for the verification of invariance under such transformations (Fig.…”

“…We note that other methods relying on smooth continuous contours (such as methods based on the use of codons or curvature scale-space, as well as many of the traditional medial-axis methods) will have great difficulty in dealing with such deformations -which are to be expected in noisy image captures and under varying environmental conditions such as due to decay and erosion. 12 To construct different databases we used the standard MPEG-7 [28], ImageCLEF-2013 [11] and Kimia (Brown University's) [47] datasets. Furthermore, we also initiated our own database where we se- 11 Our first results on such data were presented in a short paper at the 1st International Workshop on "Environmental Multimedia Retrieval" (EMR) held in conjunction with the ACM International Conference on Multimedia Retrieval (ICMR) in Glasgow (UK), April 1, 2014 [1].…”

“…12 To construct different databases we used the standard MPEG-7 [28], ImageCLEF-2013 [11] and Kimia (Brown University's) [47] datasets. Furthermore, we also initiated our own database where we se- 11 Our first results on such data were presented in a short paper at the 1st International Workshop on "Environmental Multimedia Retrieval" (EMR) held in conjunction with the ACM International Conference on Multimedia Retrieval (ICMR) in Glasgow (UK), April 1, 2014 [1]. 12 We do not claim that this way of perturbing the data is physically accurate in modeling natural decay or erosion.…”

“…From this database, we took each sample as a different query, resulting in a total comparison set of 8860×8860≈78.5 million possible pairings, and exploited our current ranking metric (F alone, or with [11]) with shape perturbations using our F − measure as the basis for ranking. NB: The first match is always the desired target.…”

A Taxonomy of Vision Systems for Ground Mobile Robots

“…This is clearly represented in the RobotVision@ImageCLEF competition [188], where participant proposals [7] have to deal with these conditions.…”