Exploration of Visual Data (The International Series in by Sean Xiang Zhou, Yong Rui, Thomas S. Huang

By Sean Xiang Zhou, Yong Rui, Thomas S. Huang

Exploration of visible Data provides most modern learn efforts within the zone of content-based exploration of snapshot and video info. the most aim is to bridge the semantic hole among high-level ideas within the human brain and low-level good points extractable by means of the machines.

The key matters emphasised are "content-awareness" and "user-in-the-loop". The authors supply a accomplished evaluation on algorithms for visible characteristic extraction in accordance with colour, texture, form, and constitution, and strategies for incorporating such info to assist looking, exploration, seek, and streaming of photograph and video info. additionally they talk about concerns regarding the combined use of textual and low-level visible positive aspects to facilitate better entry of multimedia data.

Exploration of visible Data presents state of the art fabrics at the issues of content-based description of visible information, content-based low-bitrate video streaming, and most recent uneven and nonlinear relevance suggestions algorithms, which so far are unpublished.

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For modeling local structures, we use a distance-sensitive histograming technique. In [69] or [19], the distance information is explicitly captured into the histogram bins. We argue in favor of collapsing the distance axis and instead using distance-dependent weights on the histogram increments. For example, for articulated and nonrigid objects, any constraint on the structure or distance between distant points/regions can be misleading. In this case, inverse-distance-weighted histograming can be a better choice.

Shown in part (c) are the detection results for leopard: the detection maps reveal a high likelihood region in the first test image. Second, we used six tigers as training images and simply averaged their histograms to obtain a model for "tiger," which proved as effective as a single "prototype" model. In part (e), several high likelihood regions are detected in the third test image around the face and the neck of the tiger. 49 Chapter 4. Probabilistic Local Structure Models I ('A J i t ~ 1 leA 7 I(,A 9 No ICA.

GLC&MLC) GlobalLoopCount and MaxLoopCount GlobalLoopCount is defined as sum {loop counts}. MaxLoopCount is max {loop counts}. This feature vector can capture structural information such as the windows in the build images, or it can be used for character detection and recognition. Note again that there can be many other possible ways to construct feature vectors, such as the moments offilling times, fork counts, loop counts, or water amounts, etc. Regarding scaling invariance, even though we could do normalization with regard to the image size for some features to achieve scaling invariance, the edge detector itself may not be scaling invariant.

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