A number of automated shot-change detection methods for indexing a video sequence to facilitate browsing and retrieval have been proposed. Many of these methods use color histograms or features computed from block motion or compression parameters to compute frame differences. It is important to evaluate and characterize their performance so as to deliver a single set of algorithms that may be used by other researchers for indexing video databases. We present the results of a performance evaluation and characterization of a number of shot-change detection methods that use color histograms, block motion matching, or MPEG compressed data.

Performance characterization of video-shot-change detection methods

Temporal video segmentation is the first step towards automatic annotation of digital video for browsing and retrieval. This article gives an overview of existing techniques for video segmentation that operate on both uncompressed and compressed video stream. The performance, relative merits and limitations of each of the approaches are comprehensively discussed and contrasted. The gradual development of the techniques and how the uncompressed domain methods were tailored and applied into compressed domain are considered. In addition to the algorithms for shot boundaries detection, the related topic of camera operation recognition is also reviewed.

http://www.cs.kent.edu/%7Ejaved/class-DR/sample-videosegmentation.pdf

Temporal video segmentation: A survey

Polyp-Alert

http://repository.ust.hk/ir/bitstream/1783.1-95/1/tr9716.pdf

Efficient Scene Change Detection and Camera Motion Annotation for Video Classification

This paper describes a feature based, two-pass algorithm for scene break detection in video sequences. In the first pass, the camera cuts are detected on the low resolution video using histogram comparison. The gradual changes are then detected on the high resolution video based on the editing characteristics. Experimental results indicate that the proposed discontinuous cut, dissolve and fade detection method is both computationally low and robust to local changes within the sequences.

Feature-based hierarchical video segmentation

As video information proliferates, managing video sources becomes increasingly important. Automatic video partitioning is a prerequisite for organizing and indexing video sources. Several methods have been introduced to tackle this problem, e.g., pairwise and histogram comparisons. Each has advantages, but all are slow because they entail inspection of entire images. Furthermore none of these methods have been able to define camera break and gradual transition, which are basic concepts for partitioning. In this paper, we attempt to define camera break. Then, based on our definition and probability analysis, we propose a new video partitioning algorithm, called NET Comparison (NC), which compares the pixels along predefined net lines. In this way, only part of the image is inspected during classification. We compare the effectiveness of our method with other algorithms such as pairwise, likelihood and histogram comparisons, evaluating them on the basis of a large set of varied image sequences that include camera movements, zooming, moving objects, deformed objects and video with degraded image quality. Both gray-level and HSV images were tested and our method out-performed existing approaches in speed and accuracy. On average, our method processes images two to three times faster than the best existing approach.© (1995) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Net comparison: a fast and effective method for classifying image sequences

A convenient and eficient indexing system is as essential to managing video information as it is to managing tezt. Creating an indez requires partitioning the video into meaningful segments, assigning indez terms to individual segments and combining the indez terms to form a table of contents or an inverted indez file. To date, video partitioning methods are still in the research stage. This paper introduces a new camera break detection algorithm, namely the Local Selective HSV Histogram Comparison. Color and spatial information are used in this new algorithm to complement the deficiency of the ezisting algorithms. The performance of four ezisting algorithms together with our proposed one are evaluated with image sequences of various scenarios, including camera movements, zooming, moving objects, deformable objects and video with degraded image quality. In addrtion, an adaptive thresholdtng technique is proposed to replace the general use of a single threshold for the camera break detection algorithms.

/pdf/a-robust-approach-for-camera-break-detection-in-color-video-2ov57l11pd.pdf

A robust approach for camera break detection in color video sequence

https://ranger.uta.edu/%7Eiahmad/journal-papers/[J10]%20Embedding%20Pyramids%20into%203D%20Meshes.pdf

Embedding Pyramids into 3D Meshes

Email is an important part of our lives, almost everyone has at least one email account. It is not uncommon for an average professional email user to receive/send over a hundred emails a day. With all these personal and professional messages accumulated over time, email becomes our personal archive. In recently years, many interesting techniques are developed to improve email management, visualize mailbox content, explore historical events, and discover hidden knowledge from these electronic archives. In this paper, we propose an approach to visualize and compare email archives with Streamgraphs and Tag Clouds. Our preliminary results demonstrate the capability of this technique to compare any general text corpora with temporal information.

Dixon Man-Ching Ip

Papers

Net comparison: a fast and effective method for classifying image sequences

A robust approach for camera break detection in color video sequence

Embedding Pyramids into 3D Meshes

A visual approach to text corpora comparison

A visual approach to text corpora comparison