Color histogram is the most commonly used color feature for image and video retrieval. Statistically, it denotes the joint probability of the intensities of the three color channels. Besides the color histogram, several other color feature representations have been applied in image and video retrieval, including color moments and color sets. To overcome the quantization effects in the color histogram, Stricker and Orengo proposed to use the color moments.
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To facilitate fast search over large-scale image collections, Smith and Chang proposed color sets as an approximation to the color histogram [Smith ]. Texture refers to the visual patterns that have properties of homogeneity that do not result from the presence of only a single color or intensity. It provides important structural information describing the content of many real-world images such as fruit skin, trees, clouds and fabric.
The texture features usually used to describe the visual information include spectral features, such as Gabor texture or wavelet texture, statistical features characterizing texture according to local statistical measures, such as the six Tamura texture features and the wold features. Among the six Tamura features, that is, coarseness, directionality, regularity, contrast, line-likeness, contrast and roughness, the first three are more significant, and the other three are related to the first three and have less effectiveness on texture description.
Among the various texture features, Gabor texture and wavelet texture are widely used for image retrieval and have been reported to well match the perception of human vision. Texture can also be extracted from both the entire image and the regions.
Some content based visual information retrieval applications require the shape representation of object to be invariant to translation, rotation, and scaling, while others do not. These features include aspect ratio, circularity, Fourier descriptors, moment invariants, consecutive boundary segments, etc. Besides color and texture, spatial location is also useful for region-based retrieval. There are two ways to define the spatial location: Although the global feature color, texture, edge, etc.
Many research results suggested that using layout both features and spatial relations is a better solution to image retrieval [Smith ]. To extend the global feature to a local one, a natural approach is to divide the whole image into sub-blocks and extract features from each of the sub-blocks. A variation of this approach is the quadtree-based layout approach, where the entire image was split into a quadtree structure and each tree branch had its own feature to describe its content. It is typical for audio analysis algorithms to be based on features computed on a window basis.
A wide range of audio features exist for audio computing tasks. These features can be divided into two categories: Temporal features are calculated from a set of consecutive frames or a period of time , which have two typical forms, scalar and vector. Scalar temporal features generally are statistical measures along a set of consecutive frames for visual features or a period of time for audio features , say, a shot, a scene, or a 1-second window.
Typical scalar temporal feature is the average of the features on a set of frames for visual features or a set of time windows for audio features , for example, average color histogram of the frames in a shot, and average onset rate in a scene. Another exemplary scalar temporal feature is average motion intensity and motion intensity variance within a shot. Vector temporal feature generally describes the temporal pattern or variance of a given video clip. For example, a trajectory of a moving object, curve of frame-based feature differences, camera motion speed, speaking rate, onset, etc.
Video and audio are time series, and image series can also be regarded as a time series. Media structuring is to generate table-of-content of a video or audio clip, a series of image, or a collection of video or audio clips, according to the content consistence or similarity of the media data [Zhang ]. Video segmentation, or video content structuring , is to generating temporal structure of a video sequence.
The basic unit of a video sequence is shot. A shot is defined as an uninterrupted temporal segment in a video sequence, and often defines the low-level syntactical building blocks of video content. Shots are comprised of a number of consecutive frames. A shot is generally filmed with a single camera with variable durations.
Depending on the style of transitions between two consecutive shots, shot boundaries are classified into two types, cut and graduate transitions. Cut is the most frequently used transition in video.
The common way for shot detection is to evaluate the difference between consecutive frames represented by certain feature. It is typically determined by checking the abrupt peaks in the frame difference curve, where frame difference can be obtained directly on pixels, or based on frame-based features, such as color histogram, and edge map.
Typical graduate transitions include wipe, dissolve, cross-fade, etc. As frame differences of graduate transitions changing gradually at shot boundaries, we need to check the frame difference curve for a period of time to determine whether it is a graduate transition. A typical method is the twin-comparison approach that adapts a difference metric to accommodate gradual transitions.
Professor Paolo Remagnino
Once shot boundaries are detected, video temporal structure is further analyzed using two approaches. One approach divides the shots into smaller segments, namely, sub-shots. Most typical definition is accordant with camera motions within a shot. Sub-shot is a sub segment within a shot, or we may say, each shot can be divided into one or more consecutive subshots. Sub-shots have different definitions according to different applications. Typically subshot segmentation is equivalent to camera motion detection, which means one subshot corresponds one unique camera motion.
For example, if in a shot the camera panned from left to right, and zoomed in to a specific object, then paned to the top, and zoomed out, and then stopped, then this shot consists of four sub-shots including one pan to right, on zoom in, one pan to top, and one zoom out. The other approach is to merge shots into groups of shots, i. A scene is defined as a collection of one or more adjacent shots that focus on one topic. For example, a child is playing at backyard would be one scene, even though different camera angles might be shown.
Four camera shots showing a dialog between two people may be one scene even the primary object may be different for these shot.
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- And The Angels Sing!
- Visual Event Detection (The International Series in Video Computing).
- Efficient visual event detection using volumetric features;
The similarity measure can be the same as shot detection and use the same features. More sophisticated similarity measures take multiple shots instead of two only into considerations. Generally multimodal features, including visual, audio, and even textual information closed captions or recognized speech , are employed in these tasks. Image organization is to organize a set of images according to certain structures.
A typical approach to image organization is to cluster a set of images into groups according to their content consistency or similarity. There are types basically, including time-constrained grouping and time-free grouping. Time-constrained group is similar to video scene detection, but the time-stamp can be taken into account when doing grouping. Time-free grouping actually is image clustering. Image base features, such color moment and color histogram, are applied when doing image clustering and grouping.
For personal photo collections, a higher-level objective to this issue is to temporally segment the photos into episodes or meaningful events, and sort both events and the photos within an event chronologically. Typically, event is defined as the group of photos captured in relatively close proximity in time.
Event Detection and Analysis from Video Streams
Most of photo grouping systems focused either on time or on content only, or used both but treated each in an independent way. However, a digital photo is usually recorded together with multimodal metadata such as image content perceptual features and contextual information time and camera settings.
A more sophisticated solution to event clustering of personal photos is to automatically incorporate all these multimodal metadata into a unified framework, without being provided any a prior knowledge [Mei ]. Audio segmentation, in general, is the task of segmenting an audio clip into acoustically homogenous intervals, where the rule of homogeneity depends on different applications.
As video is a time series, frequently it is difficult for viewers to grasp the main content of a video in a short period of time or a glance. Video summary offers a concise representation of the original video clips by showing the most representative synopsis of a given video sequence. Generally speaking, there are two fundamental types of video summarization: Any item with "FREE Shipping" label on the search and the product detail page is eligible and contributes to your free shipping order minimum.
You can get the remaining amount to reach the Free shipping threshold by adding any eligible item to your cart. This book is one of the first books to focus on visual event detection It demonstrates that computer vision research has matured to a point where meaningful visual event detection can be achieved The authors propose that the exact object and motion information is not necessary to achieve video.
We will send you an SMS containing a verification code. Please double check your mobile number and click on "Send Verification Code". Enter the code below and hit Verify. Free Shipping All orders of Researchers in computer graphics are increasingly employing techniques from computer vision to gen erate the synthetic imagery. A good example of this is image-based rendering and modeling techniques, in which geometry, appearance, and lighting is de rived from real images using computer vision techniques.
Here the shift is from synthesis to analysis followed by synthesis. Additional Details Series Volume Number. Table Of Content 1. Features and Classification Methods. Summary and Discussion of Alternatives. Fundamentals of Pathology by Husain A. Sattar , Paperback