This paper presents a method that embeds stability-sensitive filter in the temporal matching kernel with explicit feature maps (TE). The added filter improves the robustness of TE to noise for content-based video retrieval. Originally TE embeds temporal information of frame descriptors by using explicit feature mapping in a fixed length video vector by using a temporal invariant match kernel. TE matches relevant videos and at the same time estimates the temporal offset by referring only to these vectors. It has achieved state-of-the-art performance in video event retrieval. However, since TE defines video similarity as a sum of frame-wise similarities, we argue that TE is not so robust to noise. Usually matching videos have consecutive frames in correspondence, which result in series of high similarities. A simple sum of frame-wise similarities cannot differentiate this situation from that of non-consecutive high similarities which seems to lead to false matches. To circumvent this problem, we propose to apply a non-linear low-pass filter to the frame-wise similarities. In order to enjoy the benefit of the TE framework, we further develop a technique to embed the filter into frame descriptors. Consequently the video similarity can be evaluated as the dot product of video vectors with a stability-sensitive filter enabled. As the original TE, our method is seamlessly compatible with query expansion with temporal consistency checking. Our approach is evaluated on the EVVE dataset for particular event retrieval, it achieves state-of-the-art performance.

This paper proposes a framework for content-based video retrieval that addresses various tasks as particular event retrieval, copy detection or video synchronization. Given a video query, the method is able to efficiently retrieve, from a large collection, similar video events or near-duplicates with temporarily consistent excerpts. As a byproduct of the representation, it provides a precise temporal alignment of the query and the detected video excerpts.

In this paper a particular issue is addressed: matching still images (screen shots) with videos. A content-based similarity search approach using image queries is proposed. A fast method based on local visual patterns both for matching and indexing is employed. But we argue that using every frames may limit the scalability of the approach. Therefore only keyframes are extracted and used. The main contribution of this paper is an investigation over the trade-off between accuracy and scalability using different keyframe rates for sampling the video database. This trade-off is evaluated on a ground truth using a large reference video database (1000 hours).

Mining the video content itself can bring to light important information regarding the internal structure of large video databases, compensating for a lasting absence of extensive and reliable annotations. Many valuable links between video segments can be identified by content-based copy detection methods, where copies are transformed versions of original video sequences. To make this approach viable for large video databases, we put forward a new mining method relying on the definition of a compact keyframe-level descriptor and of a specific index structure. The performance obtained in detecting links between video segments is evaluated with the help of a ground truth and several illustrations are given. The scalability of the approach is then demonstrated for databases of up to 10,000 hours of video.