Multimedia Systems - Special issue on content-based retrieval
Maintaining knowledge about temporal intervals
Communications of the ACM
A Cognitive Assessment of Topological Spatial Relations: Results from an Empirical Investigation
COSIT '97 Proceedings of the International Conference on Spatial Information Theory: A Theoretical Basis for GIS
Semantics-Based Image Retrieval by Region Saliency
CIVR '02 Proceedings of the International Conference on Image and Video Retrieval
Indoor-Outdoor Image Classification
CAIVD '98 Proceedings of the 1998 International Workshop on Content-Based Access of Image and Video Databases (CAIVD '98)
An Overview of Content-based Image Retrieval Techniques
AINA '04 Proceedings of the 18th International Conference on Advanced Information Networking and Applications - Volume 2
Semantic Modeling of Natural Scenes for Content-Based Image Retrieval
International Journal of Computer Vision
Towards a comprehensive survey of the semantic gap in visual image retrieval
CIVR'03 Proceedings of the 2nd international conference on Image and video retrieval
Semantic image analysis using a learning approach and spatial context
SAMT'06 Proceedings of the First international conference on Semantic and Digital Media Technologies
Image classification for content-based indexing
IEEE Transactions on Image Processing
| Hi-index | 0.00 |
This paper details the behavioral evaluation of a qualitative image categorisation and retrieval approach using semantic features of images. Content based image retrieval and classification systems are highly active research areas and a cognitively plausible image description can improve effectiveness of such systems. While most approaches focus on low level image feature in order to classify images, humans, while certainly relying on some aspects of low level features, also apply high-level classifications. These high-level classification are often qualitative in nature and we have implemented a qualitative image categorisation and retrieval framework to account for human cognitive principles. While the dataset, i.e. the image database that was used for classification and retrieval purposes contained images that where annotated and therefore provided some ground truth for assessing the validity of the algorithm, we decided to add an additional behavioral evaluation step: Participants performed similarity ratings on a carefully chosen subset of picture implemented as a grouping task. Instead of using a predefined number of categories, participants could make their own choice on a) how many groups they thought were appropriate and b) which icons/images belong into these groups. The results show that the overall underlying conceptual structure created by the participants corresponds well to the classification provided through the algorithm.