Abstract
Appropriate organization of video databases is essential for pertinent indexing and retrieval of visual information. This paper proposes a new feature called Block Intensity Comparison Code (BICC) for video classification and an unsupervised shot change detection algorithm to detect the shot changes in a video stream using autoassociative neural network (AANN) which makes retrieval problems much simpler. BICC represents the average block intensity difference between blocks of a frame. A novel AANN misclustering rate (AMR) algorithm is used to detect the shot transitions. The experiments demonstrate the effectiveness of the proposed methods.
Highlights
At present, tremendous amount of digital multimedia database is accessible by people both in the Internet and television
This paper proposes a novel shot transition detection (STD) algorithm called Autoassociative Neural Network Misclustering Rate (AMR) that uses autoassociative neural network (AANN) model to detect the shots of less than 2 sec duration
This section analyzes the performance of the proposed Block Intensity Comparison Code (BICC) for video classification and AANN misclustering rate (AMR) algorithm for detecting ST
Summary
Tremendous amount of digital multimedia database is accessible by people both in the Internet and television. Research has begun to analyze the visual media to automate the retrieval task. For retrieving the video of interest, relevant organization and segmentation of the video database is important. The objective is to break up the video stream into a set of significant and handy segments called shots. A shot can be habitually visualized as a series of interconnected or unbroken sequence of successive frames taken contiguously by a single camera. A video is produced by compiling quite a few shots by a procedure called editing. Edit process constructs different kind of transitions from one shot to another such as abrupt and gradual, may take place. A survey of techniques for automatic indexing and retrieval of video data is found in Ref.[1]
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