By Panagiotis Sidiropoulos, Vasileios Mezaris, Ioannis Kompatsiaris, Hugo Meinedo (auth.), Nicola Adami, Andrea Cavallaro, Riccardo Leonardi, Pierangelo Migliorati (eds.)
Covering essentially the most state of the art learn at the supply and retrieval of interactive multimedia content material, this quantity of in particular selected contributions offers the main up to date viewpoint on one of many preferred modern themes. the fabric represents prolonged types of papers provided on the 11th overseas Workshop on photo research for Multimedia Interactive prone, a necessary foreign discussion board in this fast-moving field.
Logically geared up in discrete sections that method the topic from its quite a few angles, the content material offers in flip with content material research, movement and job research, high-level descriptors and video retrieval, 3D and multi-view, and multimedia supply. The chapters disguise the best aspect of rising innovations resembling using high-level audio info in bettering scene segmentation and using subjective common sense for forensic visible surveillance. On content material supply, the publication examines either photos and video, concentrating on key topics together with a good pre-fetching process for JPEG 2000 photo sequences. additional contributions examine new methodologies for simultaneous block reconstruction and supply a trellis-based set of rules for quicker motion-vector determination making.
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Additional resources for Analysis, Retrieval and Delivery of Multimedia Content
However, k-NN still features some major drawbacks, mainly due to the uniform voting among the nearest prototypes in the feature space. In this chapter, we propose a generalization of the classic k-NN rule in a supervised learning (boosting) framework. Namely, we redefine the voting rule as a strong classifier that linearly combines predictions from the k closest prototypes. In order to induce this classifier, we propose a novel learning algorithm, MLNN (Multiclass Leveraged Nearest Neighbors), which gives a simple procedure for performing prototype selection very efficiently.
G. spatial pyramids  or Bag-ofFeatures ) was more than merely remaining as general as possible. We carried out experiments in order to investigate the improvements brought by boosting on nearest neighbor voting. This necessitates to remove all unnecessary adjustments which could potentially interfere. Namely, we used 12 categories from the wellknown Caltech-101 database for object classification: accordion, airplanes, car side, cellphone, cup, ewer, ferry, grand piano, laptop, motorbikes, watch, Windsor chair (Fig.
6 illustrate these results with some images that exemplify the performance and limitations of the algorithm. For every example, we present the different caption text bars that have been detected. To allow analyzing which bars have been detected isolately and which ones have been detected gathered in a single component, when necessary the detected text bars are separately presented regardless of their original position in the image. 5a presents an example of non-perfectly rectangular caption text objects.