Results 1 - 10
of
81
The 2005 pascal visual object classes challenge
, 2006
"... Abstract. The PASCAL Visual Object Classes Challenge ran from February to March 2005. The goal of the challenge was to recognize objects from a number of visual object classes in realistic scenes (i.e. not pre-segmented objects). Four object classes were selected: motorbikes, bicycles, cars and peop ..."
Abstract
-
Cited by 649 (23 self)
- Add to MetaCart
(Show Context)
Abstract. The PASCAL Visual Object Classes Challenge ran from February to March 2005. The goal of the challenge was to recognize objects from a number of visual object classes in realistic scenes (i.e. not pre-segmented objects). Four object classes were selected: motorbikes, bicycles, cars and people. Twelve teams entered the challenge. In this chapter we provide details of the datasets, algorithms used by the teams, evaluation criteria, and results achieved. 1
Image retrieval: ideas, influences, and trends of the new age
- ACM COMPUTING SURVEYS
, 2008
"... We have witnessed great interest and a wealth of promise in content-based image retrieval as an emerging technology. While the last decade laid foundation to such promise, it also paved the way for a large number of new techniques and systems, got many new people involved, and triggered stronger ass ..."
Abstract
-
Cited by 485 (13 self)
- Add to MetaCart
(Show Context)
We have witnessed great interest and a wealth of promise in content-based image retrieval as an emerging technology. While the last decade laid foundation to such promise, it also paved the way for a large number of new techniques and systems, got many new people involved, and triggered stronger association of weakly related fields. In this article, we survey almost 300 key theoretical and empirical contributions in the current decade related to image retrieval and automatic image annotation, and in the process discuss the spawning of related subfields. We also discuss significant challenges involved in the adaptation of existing image retrieval techniques to build systems that can be useful in the real world. In retrospect of what has been achieved so far, we also conjecture what the future may hold for image retrieval research.
Content-based image retrieval: approaches and trends of the new age
- In Proceedings ACM International Workshop on Multimedia Information Retrieval
, 2005
"... The last decade has witnessed great interest in research on content-based image retrieval. This has paved the way for a large number of new techniques and systems, and a growing interest in associated fields to support such systems. Likewise, digital imagery has expanded its horizon in many directio ..."
Abstract
-
Cited by 91 (3 self)
- Add to MetaCart
(Show Context)
The last decade has witnessed great interest in research on content-based image retrieval. This has paved the way for a large number of new techniques and systems, and a growing interest in associated fields to support such systems. Likewise, digital imagery has expanded its horizon in many directions, resulting in an explosion in the volume of image data required to be organized. In this paper, we discuss some of the key contributions in the current decade related to image retrieval and automated image annotation, spanning 120 references. We also discuss some of the key challenges involved in the adaptation of existing image retrieval techniques to build useful systems that can handle real-world data. We conclude with a study on the trends in volume and impact of publications in the field with respect to venues/journals and sub-topics.
GaZIR: Gaze-based zooming interface for image retrieval
- in Proceedings of 11th Conference on Multimodal Interfaces and The Sixth Workshop on Machine Learning for Multimodal Interaction (ICMI-MLMI
, 2009
"... We introduce GaZIR, a gaze-based interface for browsing and searching for images. The system computes on-line predictions of relevance of images based on implicit feedback, and when the user zooms in, the images predicted to be the most relevant are brought out. The key novelty is that the relevance ..."
Abstract
-
Cited by 22 (4 self)
- Add to MetaCart
(Show Context)
We introduce GaZIR, a gaze-based interface for browsing and searching for images. The system computes on-line predictions of relevance of images based on implicit feedback, and when the user zooms in, the images predicted to be the most relevant are brought out. The key novelty is that the relevance feedback is inferred from implicit cues obtained in real-time from the gaze pattern, using an estimator learned during a separate training phase. The natural zooming interface can be connected to any content-based information retrieval engine operating on user feedback. We show with experiments on one engine that there is sufficient amount of information in the gaze patterns to make the estimated relevance feedback a viable choice to complement or even replace explicit feedback by pointing-and-clicking.
Using Long-Term Learning to Improve Efficiency of Content-Based Image Retrieval
- Proceedings of the 3 rd International Workshop on Pattern Recognition in Information Systems
, 2003
"... Abstract. Content-based image retrieval (CBIR) is an emerging research field, studying retrieval of images from unannotated databases. In CBIR, images are indexed on the basis of low-level statistical features that can be automatically derived from the images. Due to the gap between high-level seman ..."
Abstract
-
Cited by 13 (6 self)
- Add to MetaCart
(Show Context)
Abstract. Content-based image retrieval (CBIR) is an emerging research field, studying retrieval of images from unannotated databases. In CBIR, images are indexed on the basis of low-level statistical features that can be automatically derived from the images. Due to the gap between high-level semantic concepts and low-level visual features, the performance of CBIR applications often remains quite modest. One method for improving CBIR results is to try to learn the user’s preferences with intra-query learning methods such as relevance feedback. However, relevance feedback provides user interaction information which can automatically be used also in long-term or inter-query learning. In this paper, a method for using long-term learning in our PicSOM system is presented. The performed experiments show that the system readily supports the presented user interaction feature and that the efficiency of the system can be substantially increased by using it in parallel with the MPEG-7 visual descriptors.
Use of image subset features in image retrieval with self-organizing maps
- In: Proceedings of 3rd International Conference on Image and Video Retrieval (CIVR 2004
, 2004
"... Abstract. In content-based image retrieval (CBIR), the images in a database are indexed on the basis of low-level statistical features that can be automatically derived from the images. Due to the semantic gap, the performance of CBIR systems often remains quite modest especially on broad image doma ..."
Abstract
-
Cited by 13 (5 self)
- Add to MetaCart
(Show Context)
Abstract. In content-based image retrieval (CBIR), the images in a database are indexed on the basis of low-level statistical features that can be automatically derived from the images. Due to the semantic gap, the performance of CBIR systems often remains quite modest especially on broad image domains. One method for improving the results is to incorporate automatic image classification methods to the CBIR system. The resulting subsets can be indexed separately with features suitable for those particular images or used to limit an image query only to certain promising image subsets. In this paper, a method for supporting different types of image subsets within a generic framework based on multiple parallel Self-Organizing Maps and binary clusterings is presented. 1
Semantic annotation of image groups with SelfOrganizing Maps
- In: Proceedings of 4th International Conference on Image and Video Retrieval (CIVR 2005
, 2005
"... Abstract. Automatic image annotation has attracted a lot of attention recently as a method for facilitating semantic indexing and text-based retrieval of visual content. In this paper, we propose the use of multiple Self-Organizing Maps in modeling various semantic concepts and annotating new input ..."
Abstract
-
Cited by 11 (5 self)
- Add to MetaCart
(Show Context)
Abstract. Automatic image annotation has attracted a lot of attention recently as a method for facilitating semantic indexing and text-based retrieval of visual content. In this paper, we propose the use of multiple Self-Organizing Maps in modeling various semantic concepts and annotating new input images automatically. The effect of the semantic gap is compensated by annotating multiple images concurrently, thus enabling more accurate estimation of the semantic concepts ’ distributions. The presented method is applied to annotating images from a freely-available database consisting of images of different semantic categories. 1
Visual islands: intuitive browsing of visual search results
- In CIVR '08
, 2008
"... The amount of available digital multimedia has seen exponential growth in recent years. While advances have been made in the indexing and searching of images and videos, less focus has been given to aiding users in the interactive exploration of large datasets. In this paper a new framework, called ..."
Abstract
-
Cited by 10 (1 self)
- Add to MetaCart
(Show Context)
The amount of available digital multimedia has seen exponential growth in recent years. While advances have been made in the indexing and searching of images and videos, less focus has been given to aiding users in the interactive exploration of large datasets. In this paper a new framework, called visual islands, is proposed that reorganizes image query results from an initial search or even a general photo collection using a fast, non-global feature projection to compute 2D display coordinates. A prototype system is implemented and evaluated with three core goals: fast browsing, intuitive display, and non-linear exploration. Using the TRECVID2005[15] dataset, 10 users evaluated the goals over 24 topics. Experiments show that users experience improved comprehensibility and achieve a significant page-level precision improvement with the visual islands framework over traditional paged browsing.
Measuring concept similarities in multimedia ontologies: Analysis and evaluations
- IEEE Transactions on Multimedia
, 2007
"... Abstract—The recent development of large-scale multimedia concept ontologies has provided a new momentum for research in the semantic analysis of multimedia repositories. Different methods for generic concept detection have been extensively studied, but the question of how to exploit the structure o ..."
Abstract
-
Cited by 9 (2 self)
- Add to MetaCart
(Show Context)
Abstract—The recent development of large-scale multimedia concept ontologies has provided a new momentum for research in the semantic analysis of multimedia repositories. Different methods for generic concept detection have been extensively studied, but the question of how to exploit the structure of a multimedia ontology and existing inter-concept relations has not received similar attention. In this paper, we present a clustering-based method for modeling semantic concepts on low-level feature spaces and study the evaluation of the quality of such models with entropy-based methods. We cover a variety of methods for assessing the similarity of different concepts in a multimedia ontology. We study three ontologies and apply the proposed techniques in experiments involving the visual and semantic similarities, manual annotation of video, and concept detection. The results show that modeling inter-concept relations can provide a promising resource for many different application areas in semantic multimedia processing. Index Terms—Clustering-based analysis, concept detection, inter-concept relations, multimedia ontology. I.
PicSOM experiments in TRECVID 2006
- In Proceedings of the TRECVID 2006 Workshop
, 2006
"... Our experiments in TRECVID 2006 include participation in the shot boundary detection, high-level feature extraction, and search tasks, using a common system framework based on multiple parallel Self-Organizing Maps (SOMs). In the shot boundary detection task we projected feature vectors calculated f ..."
Abstract
-
Cited by 9 (9 self)
- Add to MetaCart
(Show Context)
Our experiments in TRECVID 2006 include participation in the shot boundary detection, high-level feature extraction, and search tasks, using a common system framework based on multiple parallel Self-Organizing Maps (SOMs). In the shot boundary detection task we projected feature vectors calculated from successive frames on parallel SOMs and monitored the trajectories to detect the shot boundaries. We submitted the following ten runs: • PicSOM CA: cut-optimized using all the training videos • PicSOM GA: gradual-optimized using all the training videos • PicSOM BA: optimized for both cuts and gradual transitions using all the training videos • PicSOM CN: cut-optimized using only the news videos (without the NASA videos) • PicSOM GN: gradual-optimized using only the news videos • PicSOM CS: cut-optimized using channel-specific training videos • PicSOM GS: gradual-optimized using channel-specific training videos • PicSOM CNF: cut-optimized using only the news videos and only a few features • PicSOM CNE: cut-optimized using only the news videos and one additional edge feature • PicSOM CAE: cut-optimized using all the training videos and one additional edge feature
