RETRACTED ARTICLE: Content based video retrieval system based on multimodal feature grouping by KFCM clustering algorithm to promote human–computer interaction

The video retrieval system refers to the task of retrieving the most relevant video collection, given a user query. By applying some feature extraction models the contents of the video can be extracted. With the exponential increase in video data in online and offline databases as well as a huge implementation of multiple applications in health, military, social media, and art, the Content-Based Video Retrieval (CBVR) system has emerged. The CBVR system takes the inner contents of the video frame and analyses features of each frame, through which similar videos are retrieved from the database. However, searching and retrieving the same clips from huge video collection is a hard job because of the presence of complex properties of visual data. Video clips have many frames and every frame has multiple properties that have many visual properties like color, shape, and texture. In this research, an efficient content-based video retrieval system using the AlexNet model of Convolutional Neural Network (CNN) on the keyframes system has been proposed. Firstly, select the keyframes from the video. Secondly, the color histogram is then calculated. Then the features of the color histogram are compared and analyzed for CBVR. The proposed system is based on the AlexNet model of CNN and color histogram, and extracted features from the frames are together to store in the feature vector. From MATLAB simulation results, the proposed method has been evaluated on benchmark dataset UCF101 which has 13320 videos from 101 action categories. The experiments of our system give a better performance as compared to the other state-of-the-art techniques. In contrast to the existing work, the proposed video retrieval system has shown a dramatic and outstanding performance by using accuracy and loss as performance evaluation parameters.

With the development of multimedia data types and available bandwidth there is huge demand of video retrieval systems as users shift from text based retrieval systems to content based retrieval systems (CBVRS). Selection of extracted features play an important role in content based video retrieval, regardless of video attributes under consideration. Good features selection also allows the time, space and cost saving in the retrieval process. This leads to the interest of selecting only those features that can be extracted from video data and used for indexing and retrieval along with similarity measurement methods. There are certain issues like video store, search, index and retrieve as a trend in area of content based video retrieval systems. In this paper, we have proposed a method. In this paper fractional discrete cosine transform coefficients are used to extract key frames which eventually save space and time with better completeness in the retrieval process.

In this paper, a content-based video retrieval (CBVR) system called Bounded Coordinate of Motion Histogram version 2 (BCMH v2) was processed on a distributed computing platform by using Apache Hadoop framework and a real-time distributed storage system using HBase. In fact, the amount of multimedia data is growing exponentially. Most of this data is available in image and video models. Analyzing huge data involves complex algorithms, which leads to challenges in optimizing processing time and data storage capacity. Many content-based video retrieval systems are suitable for processing large video dataset, but they are limited by the computational time and/or storage on a single machine. Thus, this paper presents the effectiveness of the proposed method with the distributed computing platform and its evaluation on the HOLLYWOOD2 video dataset. The experimental results demonstrate the good performances of the proposed approach.

With the development of multimedia data types and available bandwidth there\nis huge demand of video retrieval systems, as users shift from text based\nretrieval systems to content based retrieval systems. Selection of extracted\nfeatures play an important role in content based video retrieval regardless of\nvideo attributes being under consideration. These features are intended for\nselecting, indexing and ranking according to their potential interest to the\nuser. Good features selection also allows the time and space costs of the\nretrieval process to be reduced. This survey reviews the interesting features\nthat can be extracted from video data for indexing and retrieval along with\nsimilarity measurement methods. We also identify present research issues in\narea of content based video retrieval systems.\n

Human-computer interaction for virtual-real fusion

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.

In the paper the features of protocols and the basic methods of organization of multimedia information transfer in telecommunication networks and distributed corporate safety networks are represented. The analysis of formats of basic elements of signals used in standards of multimedia information transfer is given.

One of the most successful algorithms that bring realism in the world of 3-D generation is fast phong shading. The digital image data is rapidly expanding in quantity and heterogeneity. The traditional information retrieval techniques does not meet the user's demand, so there is need to develop an efficient system for content based image retrieval. Content based image retrieval means retrieval of images from database on the basis of visual features of image such as color, texture etc. Taylor series algorithm is use the concept of fast phong shading algorithm that is appropriate for hardware implementation. In content-based image analysis and retrieval, fast phong shading concept is used to calculate intensity. Performance is evaluated and effectiveness of our retrieval algorithm has been shown through comparative studies. A fast and simple method for rendering an object with polygon surfaces is fast phong shading. In this paper we apply fast phong shading concept to increase the retrieval rate. Content based image analysis and retrieval is very fast technology use to calculate the intensity of polygon surfaces and the flattering out dull surfaces.

Recent advances in digital video compression and networks have made videos more accessible than ever. Several content-based video retrieval systems have been proposed in the past. In this chapter, we first review these existing content-based video retrieval systems and then propose a new framework, called ClassView, to make some advances towards more efficient content-based video retrieval. This framework includes: (a) an efficient video content analysis and representation scheme to support high-level visual concept characterization; (b) a hierarchical video classification technique to bridge the semantic gap between low-level visual features and high-level semantic visual concepts; and (c) a hierarchical video database indexing structure to enable video access over large-scale database. Integrating video access with efficient database indexing tree structures has provided a great opportunity for supporting more powerful video search engines.

Digital imaging plays an important role in many human activities, such as agriculture and forest management, earth sciences, urban planning, weather forecasting, medical imaging and so on. Processing, exploring and visualizing the inconceivable volumes of such images has turned out to be progressively troublesome. The Content-Based Image Retrieval (CBIR) remains an important issue that finds potential applications, given the place that retrieving digital images similar to a user-defined specification or pattern in huge databases now occupies in the day-to-day. CBIR systems use visual information like color, shape and texture to represent images in feature vectors. In general, there is an inconsistency in the evaluation of similarity between images according to human perception and the results computed by CBIR systems, which is called Semantic Gap. One way to improve CBIR systems is by the addition of techniques to visualize and interact with CBIR regarding similarity and diversity criteria, where the user can participate more actively in the process and steer the results according to its needs. In this paper we present the Visual-Interactive k-NDN Method (ViK): a novel approach to visualize and interact with Content-Based Image Retrieval systems. This paper aims at making use of Visual Data Mining techniques applied to queries in CBIR systems, improving the interpretability of the measure of diversity, applied using fractal analysis, as well as the relevance of results according to the prior knowledge of the user. Therefore, the user takes an active role in the content-based image retrieval, guiding its result and, consequently, reducing the Semantic Gap. Additionally, a better understanding of the diversity and similarity factors involved in the query is supported by visualization and interaction techniques.

Presented in this paper is a data association method using audio and visual data which localizes targets in a cluttered environment and detects who is speaking to a robot. A particle filter is applied to efficiently select the optimal association between the target and the measurements. State variables are composed of target positions and speaking states. To update the speaking state, we first evaluate the incoming sound signal based on cross-correlation and then calculate a likelihood from the audio information. The visual measurement is used to find an optimal association between the target and the observed objects. The number of targets that the robot should interact with is updated from the existence probabilities and associations. Experimental data were collected beforehand and simulated on a computer to verify the performance of the proposed method applied to the speaker selection problem in a cluttered environment. The algorithm was also implemented in a robotic system to demonstrate reliable interactions between the robot and speaking targets.

Advancements in internet accessibility and the affordability of digital picture sensors have led to the proliferation of extensive image databases utilized across a multitude of applications. Addressing the semantic gap between low-level attributes and human visual perception has become pivotal in refining Content-Based Image Retrieval (CBIR) methodologies, especially within this context. As this field is intensely researched, numerous efficient algorithms for CBIR systems have surfaced, precipitating significant progress in the artificial intelligence field. In this study, we propose employing a hard voting ensemble approach on features derived from three robust deep learning architectures: Inception, Exception, and Mobilenet. This is aimed at bridging the divide between low-level image features and human visual perception. The Euclidean method is adopted to determine the similarity metric between the query image and the features database. The outcome was a noticeable improvement in image retrieval accuracy. We applied our approach to a practical dataset named CBIR 50, which encompasses categories such as mobile phones, cars, cameras, and cats. The effectiveness of our method was thereby validated. Our approach outshone existing CBIR algorithms with superior accuracy (ACC), precision (PREC), recall (REC), and F1-score (F1-S), proving to be a noteworthy addition to the field of CBIR. Our proposed methodology could be potentially extended to various other sectors, including medical imaging and surveillance systems, where image retrieval accuracy is of paramount importance.

Content-based video retrieval is a proper solution to handle the video data. But because of their huge volumes and high dimensionality, finding a proper way to organize them for efficient search and retrieval becomes a challenging and important problem. In this paper, we present a framework for content-based video retrieval using the Shot Cluster Tree, while the latter organizes the content of shots in the tree structure. The framework can supply users with two types of queries: query-by-content and query-by-example. We also put up a novel and efficient clustering method for generating the Shot Cluster Tree structure, where shots which are visual similar and time adjacent are grouped into shot groups using the method of Sliding Shot Window firstly and then shot groups are clustered into shot clusters with the simple agglomerative hierarchical clustering method. In addition to constructing the structure of content, the Shot Cluster Tree also provides better ways to video summary and video annotation, which facilitate the video retrieval. An experimental system has been built up. Experiments verify the effectiveness of the proposed approach.

Content Based Image Retrieval (CBIR) is a process in which for a given query image similar images will be retrieved based on the image content similarity. Image content refers to its visual features, which are mathematical representations of a digital image. The image retrieval task primarily depends on image feature extraction and similarity measurement between the feature vectors. The performance of CBIR process not only decided by the optimum features extracted from the image but also on the proper choice of the similarity/dissimilarity measures (distance metrics). As the image features broadly diversified in terms of color, texture and shape based, using the same distance metric may not work well for all of these features. In this paper, first we presented overview of geometric and statistical distance metrics used in CBIR along with the comparative analysis of these measures on color and texture features. Color features extracted by computing color histograms in HSV space and texture features by wavelet decompositions. Geometrical distances such as Manhattan, Chebyshev, Euclidean, statistical distance metrics such as Cosine Similarity, Chi-square, KullbackLeibler, Jeffrey and cumulative statistical distance metrics of Kolmogorov-Smirnov, Cramer von Mises and Earthmover's distances were analyzed for feature similarity. We gave certain conclusions on the performance of all these distance metrics in terms of Mean Average Precision (MAP) and Recall rates with color and texture features respectively.

The fast evolution of technology has led to a growing demand for multimedia data, increasing the amount of research into efficient systems to manage those materials. A lot of research has being done by the Content-Based Image Retrieval (CBIR) community in the field of images. Nowadays, they play a key role in digital applications. Thus, contextual integration of images with different sources is vital. It involves reusing and aggregating a large amount of information with other media types. In particular, if we consider video data, images can be used to summarize videos into storyboards, providing an easy way to navigate and to browse large video collections. This has been the goal of a quickly evolving research area known as video summarization. In this paper, we present a novel approach to reuse the CBIR infrastructure for searching video stories, taking advantage of the compound object (CO) concept to integrate resources. Our approach relies on a specific component technology to encapsulate the CBIR related tasks and integrate them with video summarization techniques, known as Digital Content Component (DCC). Such a strategy provides an effective way to reuse, compose, and aggregate both content and processing software.