Add to ORKGIn this paper, we present a fast and robust video search algorithm for large video database using the histogram feature which is essentially different from conventional ones. This algorithm is based on the adjacent pixel intensity difference quantization (APIDQ) algorithm, which had been reliably applied to human face recognition previously. An APIDQ histogram is utilized as the feature vector of a frame image. Combined with active search [4], a temporal pruning algorithm, fast and robust video search can be achieved. The proposed search algorithm has been evaluated by 6 hours of video to search for given 200 video clips which having a each length of 15 seconds. Experimental results show the proposed algorithm can detect the similar video c...
The pre-processing and feature extraction stages are the primary stages in object searching on video...
We present an efficient and accurate method for duplicate video detection in a large database using ...
One of the main problems with Instance-level Image Retrieval in video data is that for longer query ...
In this paper, we propose a novel fast search algorithm for short MPEG video clips from video databa...
In this paper, we propose an improved fast search algorithm using combined histogram features and te...
In this paper, we present an improved fast and robust search algorithm for copy detection using hist...
In this paper a fast and robust method is proposed to search a large video collection for given shor...
The need for image based video search is increasing rapidly as today with the expansion of big data ...
This paper presents a very simple yet highly reliable face recognition algorithm using Adjacent Pixe...
The increasing use of multimedia applications nowadays necessitates the development of effective met...
We study the challenges of image-based retrieval when the database consists of videos. This variatio...
The problem of finding the point in time at which a known audio or video source (reference signal) a...
The objective of this work is to provide a simple and yet efficient tool to detect human faces in vi...
This contribution addresses the task of searching for faces in large video datasets. Despite vast pr...
Three different frame features (color patches, color co-herence vectors, and gradient histograms) ar...
The pre-processing and feature extraction stages are the primary stages in object searching on video...
We present an efficient and accurate method for duplicate video detection in a large database using ...
One of the main problems with Instance-level Image Retrieval in video data is that for longer query ...
In this paper, we propose a novel fast search algorithm for short MPEG video clips from video databa...
In this paper, we propose an improved fast search algorithm using combined histogram features and te...
In this paper, we present an improved fast and robust search algorithm for copy detection using hist...
In this paper a fast and robust method is proposed to search a large video collection for given shor...
The need for image based video search is increasing rapidly as today with the expansion of big data ...
This paper presents a very simple yet highly reliable face recognition algorithm using Adjacent Pixe...
The increasing use of multimedia applications nowadays necessitates the development of effective met...
We study the challenges of image-based retrieval when the database consists of videos. This variatio...
The problem of finding the point in time at which a known audio or video source (reference signal) a...
The objective of this work is to provide a simple and yet efficient tool to detect human faces in vi...
This contribution addresses the task of searching for faces in large video datasets. Despite vast pr...
Three different frame features (color patches, color co-herence vectors, and gradient histograms) ar...
The pre-processing and feature extraction stages are the primary stages in object searching on video...
We present an efficient and accurate method for duplicate video detection in a large database using ...
One of the main problems with Instance-level Image Retrieval in video data is that for longer query ...