By Angel D. Sappa, Jordi Vitrià
Traditional development reputation (PR) and machine imaginative and prescient (CV) applied sciences have regularly keen on complete automation, although complete automation frequently proves elusive or unnatural in lots of purposes, the place the expertise is anticipated to aid instead of substitute the human brokers. even if, now not the entire difficulties may be immediately solved being the human interplay the one solution to take on these purposes.
Recently, multimodal human interplay has turn into a major box of accelerating curiosity within the study group. complex man-machine interfaces with excessive cognitive functions are a sizzling study subject that goals at fixing not easy difficulties in snapshot and video functions. truly, the belief of laptop interactive structures used to be already proposed at the early levels of laptop technology. these days, the ubiquity of photo sensors including the ever-increasing computing functionality has open new and tough possibilities for learn in multimodal human interaction.
This ebook goals to teach how current PR and CV applied sciences can obviously evolve utilizing this new paradigm. The chapters of this publication convey various winning case reviews of multimodal interactive applied sciences for either photograph and video functions. They conceal a large spectrum of purposes, starting from interactive handwriting transcriptions to human-robot interactions in genuine environments.
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Additional info for Multimodal Interaction in Image and Video Applications
Users are asked to find a given target image with the image retrieval system. The test is performed on the PASCAL data set. We compare the retrieval system with only visual image description (V-system) to the system with both visual and semantic information (VS-system). As an evaluation measure we compare the number of target images which were found within X rounds of interaction. Since in the VS-system the user can also balance the relative weight of semantic and visual information, we allow more rounds of interaction to the V-system.
This method combines both cue binding and cue weighting into a single representation. However, a disadvantage of this representation is that it does not possess scalability with the number of categories. Therefore, this method is not suitable for large class problems. An overview of the properties of the several methods to combine various cues in bag-of-words is given in Table 1. In the Interactive Visual and Semantic Image Retrieval 37 Fig. 4 Example of Portmanteau vocabulary: six different clusters are shown for the SUN data set, where every cluster is represented by 100 randomly sampled patches which are assigned to the cluster.
Pattern Recognition 37(1), 1–19 (2004) Interactive Visual and Semantic Image Retrieval Joost van de Weijer, Fahad Khan, and Marc Masana Abstract. One direct consequence of recent advances in digital visual data generation and the direct availability of this information through the World-Wide Web, is a urgent demand for efficient image retrieval systems. The objective of image retrieval is to allow users to efficiently browse through this abundance of images. Due to the non-expert nature of the majority of the internet users, such systems should be user friendly, and therefore avoid complex user interfaces.