In this article, we study the efficiency problem of video stream near-duplicate monitoring in a large-scale repository. Existing stream monitoring methods are mainly designed for a short video to scan over a query stream; they have difficulty being scalable for a large number of long videos. We present a simple but effective algorithm called incremental similarity update to address the problem. That is, a similarity upper bound between two videos can be calculated incrementally by leveraging the prior knowledge of the previous calculation. The similarity upper bound takes a lightweight computation to filter out unnecessary time-consuming computation for the actual similarity between two videos, making the search process more efficient. We integrate the algorithm with inverted indexing to obtain a candidate list from the repository for the given query stream. Meanwhile, the algorithm is applied to scan each candidate for locating exact near-duplicate subsequences. We implement several state-of-the-art methods for comparison in terms of accuracy, execution time, and memory consumption. Experimental results demonstrate the proposed algorithm yields comparable accuracy, compact memory size, and more efficient execution time.

Sports video annotation, an active research area in the field of multimedia content understanding, is an essential process in applications, such as summarization, highlight extraction, event detection, and retrieval. This paper considers the issue in relation to the annotation of baseball videos. Conventional baseball video annotation frameworks are based primarily on video content analysis, such as scoreboard recognition and machine learning techniques, which require a substantial amount of human input to collect and organize training data. The performance of such frameworks might become unstable if they encounter audiovisual patterns not included in the training data. To address the issue, we propose a novel framework for baseball video annotation that aligns high-level webcast text with low-level video content. Several cues, which are derived from the video content and webcast text, are utilized for alignment by leveraging hierarchical agglomerative clustering and genetic algorithm optimization. In addition, we develop an unsupervised method to learn the pitch segment properties of baseball videos by Markov random walk, and thereby reduce the need for human intervention substantially. Our experiments demonstrate that the proposed framework yields a robust result against a variety of video content and enhances the automaticity in baseball video annotation.

In this paper, we present a novel near-duplicate video detection approach based on video query reformulation to expedite the video subsequence search process. The proposed video query reformulation method addresses two key issues: 1) how to efficiently skip unnecessary subsequence matches and 2) how to effectively increase the skip probability. First, we present an incremental update mechanism that rapidly estimates the similarity between two video subsequences to skip unnecessary matches. Second, we formulate an optimization problem of subsequence partition to increase the skip probability; a trust-region-based gradient descent algorithm is applied to solve the optimization problem. Extensive experiments cover various feature representations, subsequence granularities, and baseline methods; the results demonstrate that the proposed query reformulation method is robust and efficient to deal with a variety of near-duplicates in a large-scale video dataset.