KEYWORDS: Video, Video coding, Visualization, Computer programming, Visual process modeling, Distortion, Video compression, Semantic video, Digital filtering, Radon
MPEG-4 treats a scene as a composition of several objects or so-called video object planes (VOPs) that are separately encoded and decoded. Such a flexible video coding framework makes it possible to code different video object with different distortion scale. It is necessary to analyze the priority of the video objects according to its semantic importance, intrinsic properties and psycho-visual characteristics such that the bit budget can be distributed properly to video objects to improve the perceptual quality of the compressed video. This paper aims to provide an automatic video object priority definition method based on object-level visual attention model and further propose an optimization framework for video object bit allocation. One significant contribution of this work is that the human visual system characteristics are incorporated into the video coding optimization process. Another advantage is that the priority of the video object can be obtained automatically instead of fixing weighting factors before encoding or relying on the user interactivity. To evaluate the performance of the proposed approach, we compare it with traditional verification model bit allocation and the optimal multiple video object bit allocation algorithms. Comparing with traditional bit allocation algorithms, the objective quality of the object with higher priority is significantly improved under this framework. These results demonstrate the usefulness of this unsupervised subjective quality lifting framework.
Digital image retrieval systems allow sophisticated querying and searching by image contents. Since 1990’s, Content-Based Image Retrieval (CBIR) has attracted great research attention. In this paper, we propose a new approach for shape-based image retrieval. We perform an independent edge self-reinforcement algorithm on the edge map to yield the salient edges. The content of a salient edge is characterized by its low-level features, including length, rotation angle histogram and corner frequency. Then, the image similarity measure are based on the EMD (Earth Mover’s Distance), named as integrated salient edge matching in this article. Preliminary experimental results on a database containing 7000 images demonstrate that the proposed method is promising.
Content-based image retrieval has become an active research topic for more than one decade. Nevertheless, current image retrieval systems still have major difficulties bridging the gap between the user’s implied concept and the low-level image description. To address the difficulties, this paper presents a novel image retrieval model integrating long-term learning with short-term learning. This model constructs a semantic image link network by long-term learning which simply accumulates previous users’ relevance feedback. Then, the semantic information learned from long-term learning process guides short-term learning of a new user. The image retrieval is based on a seamless joint of both long-term learning and short-term learning. The model is easy to implement and can be efficiently applied to a practical image retrieval system. Experimental results on 10,000 images demonstrate that the proposed model is promising.
Image retrieval is the hot point of researchers in many domains. Traditional text-based query methods use caption and keywords to annotate and retrieval image database, which often consumes a mass of human labor. Content based image retrieval methods use low-level features such as, color, shape and texture to search images, which can't provide retrieval on semantic level for users. In this paper, we propose a novel image retrieval model that provides users with both semantics based query and visual features based query. Our approach has several advantages. First, it integrates visual features and semantics seamlessly. Second, it uses some effective techniques such as image classification, relevance feedback to bridge the gap between visual features and semantics. Third, it proposes several ways to obtain the semantic information of the image, which reduces manual labor and reduces the "subjectivity" of semantics by human. Fourth, it can update semantics of the image by human's intervention, which makes the image retrieval more flexible. We have implemented an image retrieval system ImageSearch based on our proposed image retrieval approach. Experiments on an image database containing 22000 show that our scheme can achieve high efficiency.
Content-based image retrieval (CBIR) is becoming a hot research point in the field of multimedia information retrieval. Interest points are local features with high informational content. So, this paper proposes a novel method for image retrieval using interest points, which contains three key stages: interest points detection, image features description based on interest points and similarity measure between two images. In the process of detecting interest points, firstly, we use a self-adaptive filter to smooth image, and then use detector to find interest points. In the stage of image features description, we design a histogram to represent image, which contains local gray changes of interest points, mutual position relations among interest points and interest points distribution of the whole image. In the stage of similarity measure, we use the distance between two histograms to calculate similarity between two images. Lots of experimental results based on a database containing 1500 images demonstrate our proposed approach is efficient.
The rapidly increasing usage of multimedia environments has led to a greater demand for image retrieval. In this paper, we propose a method for image database retrieval based on salient edges. It achieves both the desired efficiency and accuracy using a three-stage: in the first stage, we extract edge points from the original image and link them to edge curves; in the second stage, we select salient edges according to their lengths, and com*pute rotational angle histogram (RAH) and corners' average frequency (CAF) for every salient edge; in the last stage, a feature vector is generated based on those RAHs and CAFs. We have tested this technique using an image database containing more than 4000 images and all results show that our scheme can perform retrieval efficiently. When an image database is on the order of tens of thousands of images, suitable indexing methods become critical for efficient query processing. This paper also present a new indexing method called tree structured triangle inequality (TSTI), which combines triangle inequality indexing method with tree structured indexing technique. The experiments provide evidence that our proposed method can improve the retrieval speed but not reduce its accuracy.
How to extract the edge effectively in noisy image is a difficult problem of pattern recognition. In this paper, we present a stochastic heuristic search algorithm to extract edge in noisy image. We use repetitive random searches to obtain various possible independent-edge trajectories in the edge image, then self-reinforce and accumulate the search trajectories respectively, at last, extract the edges based on the results of the accumulation of self-reinforcement. Our technique combines the local information of the edge points and the whole information of independent-edge curves availably. Lots of experiments and comparing with past heuristic search algorithms, we find that our method can extract edges effectively and suppress the noise.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.