Chih-Yi Chiu

A Framework for Handling Spatiotemporal Variations in Video Copy Detection

An effective video copy detection framework should be robust against spatial and temporal variations, e.g., changes in brightness and speed. To this end, a content-based approach for video copy detection is proposed. We define the problem as a partial matching problem in a probabilistic model and transform it into a shortest-path problem in a matching graph. To reduce the computation costs of the proposed framework, we introduce some methods that rapidly select key frames and candidate segments from a large amount of video data. The experiment results show that the proposed approach not only handles spatial and temporal variations well, but it also reduces the computation costs substantially.

A Time Warping Based Approach for Video Copy Detection

The proliferation of digital video urges the need of video copy detection for content and rights management. An efficient video copy detection technique should be able to deal with spatiotemporal variations (e.g., changes in brightness or frame rates), and lower down the computation cost. While most studies put more emphases on spatial variations, less effort is made for temporal variations and computation cost. To address the above issues, we propose a time warping based approach for video copy defection. A time warping matching algorithm is used to deal with video temporal variations. To reduce matching times, a fast filtering method to generate key frames and select candidate clips from video is presented. Our experiments demonstrate promising results of the proposed approach

Fast min-hashing indexing and robust spatio-temporal matching for detecting video copies

The increase in the number of video copies, both legal and illegal, has become a major problem in the multimedia and Internet era. In this article, we propose a novel method for detecting various video copies in a video sequence. To achieve fast and robust detection, the method fully integrates several components, namely the min-hashing signature to compactly represent a video sequence, a spatio-temporal matching scheme to accurately evaluate video similarity compiled from the spatial and temporal aspects, and some speedup techniques to expedite both min-hashing indexing and spatio-temporal matching. The results of experiments demonstrate that, compared to several baseline methods with different feature descriptors and matching schemes, the proposed method which combines both global and local feature descriptors yields the best performance when encountering a variety of video transformations. The method is very fast, requiring approximately 0.06 seconds to search for copies of a thirty-second video clip in a six-hour video sequence.

Efficient and Effective Video Copy Detection Based on Spatiotemporal Analysis

In this paper, a novel method is presented to detect video copies for a given video query. These copies and the query have identical or near-duplicate content, which might differ in their spatiotemporal structures slightly. To address both the efficient and effective issues, we conduct the bag-of words model for video feature representation, and apply a coarse-to-fine matching scheme to analyze the video spatiotemporal structure. The proposed method can deal with various kinds of video transformations, such as cropping, zooming, speed change, and subsequence insertion/deletion, which are not well addressed in existing methods. Besides, two indexing methods are employed to speed up the matching process. Experimental results show that the proposed method can behave in an efficient and effective manner.

Tagging Webcast Text in Baseball Videos by Video Segmentation and Text Alignment

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.

Time-Series Linear Search for Video Copies Based on Compact Signature Manipulation and Containment Relation Modeling

This paper presents a novel time-series linear search (TLS) method for detecting video copies. The method utilizes a sliding window to locate window sequences that are near-duplicates of a given query sequence. We address two issues of the conventional TLS method in order to strengthen its video copy detection capability. First, to accelerate the TLS process, we use a sequence-level signature as a compact representation of a video sequence based on the min-hash theory, and develop an efficient heap manipulation technique for fast generation of each window sequences signature. Second, to improve the robustness of the TLS method, we use two techniques, namely, window length estimation and threshold transform, to resolve the containment relation problem caused by various types of video transformation and editing, such as frame cropping and speed change. The results of experiments on the MUSCLE-VCD-2007 dataset demonstrate that the proposed method is efficient and robust against different types of video transformation and editing.

Finding Event-Relevant Content from the Web Using a Near-Duplicate Detection Approach

In online resources, such as news and weblogs, authors often extract articles, embed content, and comment on existing articles related to a popular event. Therefore, it is useful if authors can check whether two or more articles share common parts for further analysis, such as cocitation analysis and search result improvement. If articles do have parts in common, we say the content of such articles is event-relevant. Conventional text classification methods classify a complete document into categories, but they cannot represent the semantics precisely or extract meaningful event-relevant content. To resolve these problems, we propose a near-duplicate detection approach for finding event-relevant content in Web documents. The efficiency of the approach and the proposed duplicate set generation algorithms make it suitable for identifying event-relevant content. The experiment results demonstrate the potential of the proposed approach for use in weblogs.

Image Content Clustering and Summarization for Photo Collections

Rapid growth of digital photography in recent years spurred the need of photo management tools. In this study, we propose an automatic organization framework for photo collections based on image content, so that a novel browsing experience is provided for users. For each photograph, human faces, together with corresponding clothes and nearby regions are located. We extract color histograms of these regions as the image content feature. Then a similarity matrix of a photo collection is generated according to temporal and content features of those photographs. We perform hierarchical clustering based on this matrix, and extract duplicate subjects of a cluster by introducing the contrast context histogram (CCH) technique. The experimental results show that the developed framework provides a promising result for photo management

Background music identification through content filtering and min-hash matching

A novel framework for background music identification is proposed in this paper. Given a piece of audio signals that mixes background music with speech/noise, we identify the music part with source music data. Conventional methods that take the whole audio signals for identification are inappropriate in terms of efficiency and accuracy. In our framework, the audio content is filtered through speech center cancellation and noise removal to extract clear music segments. To identify these music segments, we use a compact feature representation and efficient similarity measurement based on the min-hash theory. The results of experiments on the RWC music database show a promising direction.

Availability and immobilization of 137Cs in subtropical high mountain forest and grassland soils.

To understand the behavior of (137)Cs in undisturbed soils after nuclear fallout deposition between the 1940s and 1980s, we investigated the speciation of (137)Cs in soils in forest and its adjacent grassland from a volcano and subalpine area in Taiwan. We performed sequential extraction of (137)Cs (i.e., fractions readily exchangeable, bound to microbial biomass, bound to Fe-Mn oxides, bound to organic matter, persistently bound and residual). For both the forest and grassland soils, (137)Cs was mainly present in the persistently bound (31-41%) and residual (22-62%) fractions. The proportions of (137)Cs labile fractions--bound to exchangeable sites, microbial biomass, Mn-Fe oxides, and organic matter--were lower than those of the recalcitrant fractions. The labile fractions in the forest soils were also higher than those in the grassland soils, especially in the volcanic soil. The results suggest that the labile form of (137)Cs was mostly transferred to the persistently bound and resistant fractions after long-term deposition of fallout. The readily exchangeable (137)Cs fraction was higher in soils with higher organic matter content or minor amounts of 2:1 silicate clay minerals.