The disclosure is directed to techniques for automatic segmentation of a region-of-interest (ROI) video object from a video sequence. ROI object segmentation enables selected ROI or “foreground” objects of a video sequence that may be of interest to a viewer to be extracted from non-ROI or “background” areas of the video sequence. Examples of a ROI object are a human face or a head and shoulder area of a human body. The disclosed techniques include a hybrid technique that combines ROI feature detection, region segmentation, and background subtraction. In this way, the disclosed techniques may provide accurate foreground object generation and low-complexity extraction of the foreground object from the video sequence. A ROI object segmentation system may implement the techniques described herein. In addition, ROI object segmentation may be useful in a wide range of multimedia applications that utilize video sequences, such as video telephony applications and video surveillance applications.

Multi-mode region-of-interest video object segmentation

Mean shift-based clustering

Mean Shift tracking with multiple reference color histograms

The optimal exploitation of the information provided by hyperspectral images requires the development of advanced image-processing tools. This paper proposes the construction and the processing of a new region-based hierarchical hyperspectral image representation relying on the binary partition tree (BPT). This hierarchical region-based representation can be interpreted as a set of hierarchical regions stored in a tree structure. Hence, the BPT succeeds in presenting: 1) the decomposition of the image in terms of coherent regions, and 2) the inclusion relations of the regions in the scene. Based on region-merging techniques, the BPT construction is investigated by studying the hyperspectral region models and the associated similarity metrics. Once the BPT is constructed, the fixed tree structure allows implementing efficient and advanced application-dependent techniques on it. The application-dependent processing of BPT is generally implemented through a specific pruning of the tree. In this paper, a pruning strategy is proposed and discussed in a classification context. Experimental results on various hyperspectral data sets demonstrate the interest and the good performances of the BPT representation.

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Hyperspectral Image Representation and Processing With Binary Partition Trees

Cross-bin metrics have been shown to be more suitable than bin-by-bin metrics for measuring the distance between histograms in various applications. In particular, a visual tracker that minimizes the earth mover's distance (EMD) between the candidate and reference feature histograms has recently been proposed. This tracker was shown to be more robust than the Mean Shift tracker, which employs a bin-by-bin metric. In each frame, the former tracker iteratively shifts the candidate location by one pixel in the direction opposite to the EMD's gradient until no improvement is made. This optimization process involves the clustering of the candidate feature density in feature space, as well as the computation of the EMD between the candidate and reference feature histograms after each shift of the candidate location. In this paper, alternative trackers that employ cross-bin metrics as well, but that are based on Mean Shift (MS) iterations, are derived. The proposed trackers are simpler and faster due to 1) the use of MS-based optimization, which is not restricted to single pixel shifts, 2) abstention from any clustering of feature densities, and 3) abstention from EMD computations in multidimensional spaces.

Mean Shift Trackers with Cross-Bin Metrics

Mean shift blob tracking with kernel histogram filtering and hypothesis testing

This paper presents an efficient face segmentation algorithm based on binary partition tree. Skin-like regions are first obtained by integrating the results of pixel classification and watershed segmentation. Facial features are extracted by the techniques of valley detection and entropic thresholding, and are used to refine the skin-like regions. In order to segment the facial regions from the skin-like regions, a novel region merging algorithm is proposed by considering the impact of the common border ratio between adjacent regions, and the binary partition tree is used to represent the whole region merging process. Then the facial likeness of each node in the binary partition tree is evaluated using a set of fuzzy membership functions devised for a number of facial primitives of geometrical, elliptical and facial features. Finally, an efficient algorithm of node selecting in the binary partition tree is proposed for the final face segmentation, which can exactly segment the faces without any underlying assumption. The performance of the proposed face segmentation algorithm is demonstrated by experimental results carried out on a variety of images in different scenarios.

An efficient face segmentation algorithm based on binary partition tree

Semi-automatic video object segmentation using seeded region merging and bidirectional projection

Classic mean-shift trackers have no integrated scale adaptation, which limits their performance in tracking variable scale objects. By analyzing the similarity of object kernel histograms, we found that the changes of object scale and position within the fixed kernel make the Bhattacharyya coefficient monotonic decreasing. The work plays a guiding role in solving scaling problems within the mean-shift framework. © 2005 Society of Photo-Optical Instrumentation Engineers.

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Ning Song Peng

Papers

Mean shift blob tracking with kernel histogram filtering and hypothesis testing

An efficient face segmentation algorithm based on binary partition tree

Semi-automatic video object segmentation using seeded region merging and bidirectional projection

Performance analysis for tracking of variable scale objects using mean-shift algorithm