Human Face Detection in Color Images

Detection of human face has many realistic and important applications such as human and computer interface, face recognition, face image database management, security access control systems and content-based indexing video retrieval systems. In this report a face detection scheme will be presented. The scheme is designed to operate on color images. In the first stage of algorithm, the skin color regions are detected based on the chrominance information. A color segmentation stage is then employed to make skin color regions to be divided into smaller regions which have homogenous color. Then, we use the iterative luminance segmentation to further separate the detected skin region from other skin-colored objects such as hair, clothes, and wood, based on the high variance of the luminance component in the neighborhood of edges of objects. Post-processing is applied to determine whether skin color regions fit the face constrains on density of skin, size, shape and symmetry and contain the facial features such as eyes and mouths. Experimental results show that the algorithm is robust and is capable of detecting multiple faces in the presence of a complex background which contains the color similar to the skin tone.

Human Face Detection in Color Images

Detection of human face has many realistic and important applications such as human and computer interface, face recognition, face image database management, security access control systems and content-based indexing video retrieval systems. In this report a face detection scheme will be presented. The scheme is designed to operate on color images. In the first stage of algorithm, the skin color regions are detected based on the chrominance information. A color segmentation stage is then employed to make skin color regions to be divided into smaller regions which have homogenous color. Then, we use the iterative luminance segmentation to further separate the detected skin region from other skin-colored objects such as hair, clothes, and wood, based on the high variance of the luminance component in the neighborhood of edges of objects. Post-processing is applied to determine whether skin color regions fit the face constrains on density of skin, size, shape and symmetry and contain the facial features such as eyes and mouths. Experimental results show that the algorithm is robust and is capable of detecting multiple faces in the presence of a complex background which contains the color similar to the skin tone.

Content Based Video Retrieval by Using Distributed Real-Time System Based on Storm

Time processing is a challenging issue for content-based video retrieval systems, especially when the process of indexing, classifying and retrieving desired and relevant videos is from a huge database. A CBVR system called bounded coordinate of motion histogram (BCMH) has been implemented as a case study. The BCMH offline step requires a long time to complete the learning phase, and the online step falls short in addressing the real-time video processing. To overcome these drawbacks, this article presents a batch-oriented computing based on Apache Hadoop to improve the time processing for the offline step, and a real-time oriented computing based on Apache Storm topologies to achieve a real-time response for the online step. The proposed approach is tested on the HOLLYWOOD2 dataset and the obtained results demonstrate reliability and efficiency of the proposed method.

Derivations of design concepts for video search interfaces

PurposeKeywords search is intuitive, simple to use and convenient. It is also thede factoinput interface for textual and multimedia retrieval. However, individuals often perform poorly when faced with exploratory search tasks that are common during learning, resulting in poor quality searches. The purpose of this paper is to examine how adolescent learners search and select videos to support self-learning. The findings allow for the identification of design concepts of video retrieval interface and features that can facilitate better exploratory searches.Design/methodology/approachParticipants were assigned two customized video search tasks. The think-aloud protocol is used to allow participants to verbalize their actions, thoughts and feeling. This approach offered rich insights to the participants’ cognitive processes and considerations when performing the search tasks.FindingsThis study identified five themes for exploratory video search behavior: selection of internet resources, query formulation/reformulation, selection of the video(s) for preview, getting acquainted with the video content, and making a decision for the search task. The analysis of these themes led to a number of design concepts, ranging from supporting exploration of topics to better interaction with metadata.Practical implicationsThe findings can inform future development of dedicated video retrieval systems interfaces that seeks to facilitate effective exploratory searches by learners.Originality/valueThis study contributes by suggesting design concepts for video retrieval system developers to support exploratory video searches.

Content Based Video Retrieval Systems with Local Features: A Survey

With the improvement of mixed media information composes and accessible transfer speed there is immense interest of video retrieving frameworks, as clients move from content based recovery frameworks to content based retrieval frameworks. Determination of removed features assume an imperative job in substance based video retrieving paying little mind to video qualities being under thought. This work assists the up and coming analysts in the field of video retrieving with getting the thought regarding distinctive procedures and strategies accessible for the video recovery. These highlights are proposed for choosing, ordering and positioning as indicated by their potential enthusiasm to the client. Great feature determination likewise permits the time and space expenses of the recovery procedure to be lessened. This overview surveys the fascinating highlights that can be separated from video information for ordering and retrieving alongside likeness estimation techniques. We likewise recognize present research issues in territory of content based video retrieving frameworks.

Content Coverage and Redundancy Removal in Video Summarization

Over the past decade, research in the field of Content-Based Video Retrieval Systems (CBVRS) has attracted much attention as it encompasses processing of all the other media types i.e. text, image and audio. Video summarization is one of the most important applications as it potentially enables efficient and faster browsing of large video collections. A concise version of the video is often required due to constraints in viewing time, storage, communication bandwidth as well as power. Thus, the task of video summarization is to effectively extract the most important portions of the video, without sacrificing the semantic information in it. The results of video summarization can be used in many CBVRS applications like semantic indexing, video surveillance copied video detection etc. However, the quality of the summarization task depends on two basic aspects: content coverage and redundancy removal. These two aspects are both important and contradictory to each other. This chapter aims to provide an insight into the state-of-the-art approaches used for this booming field of research.