The huge amount of video data on the internet requires efficient video browsing and retrieval strategies. One of the viable solutions is to provide summaries of the videos in the form of key frames. The video summarization using visual attention modeling has been used of late. In such schemes, the visually salient frames are extracted as key frames on the basis of theories of human attention modeling. The visual attention modeling schemes have proved to be effective in video summarization. However, the high computational costs incurred by these techniques limit their applicability in practical scenarios. In this context, this paper proposes an efficient visual attention model based key frame extraction method. The computational cost is reduced by using the temporal gradient based dynamic visual saliency detection instead of the traditional optical flow methods. Moreover, for static visual saliency, an effective method employing discrete cosine transform has been used. The static and dynamic visual attention measures are fused by using a non-linear weighted fusion method. The experimental results indicate that the proposed method is not only efficient, but also yields high quality video summaries.

Efficient visual attention based framework for extracting key frames from videos

A novel saliency detection algorithm for video sequences based on the random walk with restart (RWR) is proposed in this paper. We adopt RWR to detect spatially and temporally salient regions. More specifically, we first find a temporal saliency distribution using the features of motion distinctiveness, temporal consistency, and abrupt change. Among them, the motion distinctiveness is derived by comparing the motion profiles of image patches. Then, we employ the temporal saliency distribution as a restarting distribution of the random walker. In addition, we design the transition probability matrix for the walker using the spatial features of intensity, color, and compactness. Finally, we estimate the spatiotemporal saliency distribution by finding the steady-state distribution of the walker. The proposed algorithm detects foreground salient objects faithfully, while suppressing cluttered backgrounds effectively, by incorporating the spatial transition matrix and the temporal restarting distribution systematically. Experimental results on various video sequences demonstrate that the proposed algorithm outperforms conventional saliency detection algorithms qualitatively and quantitatively.

Spatiotemporal Saliency Detection for Video Sequences Based on Random Walk With Restart

A deep-learning based feature hybrid framework for spatiotemporal saliency detection inside videos

Saliency detection aims at quantitatively predicting attended locations in an image. It may mimic the selection mechanism of the human vision system, which processes a small subset of a massive amount of visual input while the redundant information is ignored. Motivated by the biological evidence that the receptive fields of simple cells in V1 of the vision system are similar to sparse codes learned from natural images, this paper proposes a novel framework for saliency detection by using image sparse coding representations as features. Unlike many previous approaches dedicated to examining the local or global contrast of each individual location, this paper develops a probabilistic computational algorithm by integrating objectness likelihood with appearance rarity. In the proposed framework, image sparse coding representations are yielded through learning on a large amount of eye-fixation patches from an eye-tracking dataset. The objectness likelihood is measured by three generic cues called compactness, continuity, and center bias. The appearance rarity is inferred by using a Gaussian mixture model. The proposed paper can serve as a basis for many techniques such as image/video segmentation, retrieval, retargeting, and compression. Extensive evaluations on benchmark databases and comparisons with a number of up-to-date algorithms demonstrate its effectiveness.

An Object-Oriented Visual Saliency Detection Framework Based on Sparse Coding Representations

Video summarization (VSUMM) has become a popular method in processing massive video data. The key point of VSUMM is to select the key frames to represent the effective contents of a video sequence. The existing methods can only extract the static images of videos as the content summarization, but they ignore the representation of motion information. To cope with these issues, a novel framework for an efficient video content summarization as well as video motion summarization is proposed. Initially, Capsules Net is trained as a spatiotemporal information extractor, and an inter-frames motion curve is generated based on those spatiotemporal features. Subsequently, a transition effects detection method is proposed to automatically segment the video streams into shots. Finally, a self-attention model is introduced to select key-frames sequences inside the shots; thus, key static images are selected as video content summarization, and optical flows can be calculated as video motion summarization. The ultimate experimental results demonstrate that our method is competitive on VSUMM, TvSum, SumMe, and RAI datasets about shot segmentation and video content summarization, and can also represent a good motion summarization result.

A Novel Key-Frames Selection Framework for Comprehensive Video Summarization

A visual attention index descriptor based on a visual attention model bridges the semantic gap between low-level descriptors used by computers and high-level concepts perceived by humans.

Jiang Peng

Papers

Keyframe-Based Video Summary Using Visual Attention Clues