Content-based Video Retrieval Based on Similarity of Camera Motion

ABSTRACTThe proposed content-based video retrieval method in this study uses feature extraction, shot boundary detection, shot noise filter, shot cluster, the key feature set of shot extraction and shot classification. Shot boundary detection is conducted using a weight selection genetic algorithm. In order to verify the video retrieval method in this paper, three video databases were used in the experiment. These video databases are used to verify the shot boundary detection, the shot classification and video retrieval of this study. A series of comparisons and analyses were then conducted using the above video databases. The experiment results showed that the shot boundary detection method exhibits better performance than the other compared methods. Moreover, the video retrieval experiment results showed that the proposed method is able to precisely catch a shot in the search video, and do so with reduced video retrieval time.

Video is a rich information source containing both audio and visual information along with motion information embedded in it. Applications such as e-learning, live TV, video on demand, traffic monitoring, etc. need an efficient video retrieval strategy. Content-based video retrieval and superpixel segmentation are two diverse application areas of computer vision. In this work, we are presenting an algorithm for content-based video retrieval with help of Integration of Curvelet transform and Simple Linear Iterative Clustering (ICTSLIC) algorithm. Proposed algorithm consists of two steps: off line processing and online processing. In offline processing, keyframes of the database videos are extracted by employing features: Pearson Correlation Coefficient (PCC) and color moments (CM) and on the extracted keyframes superpixel generation algorithm ICTSLIC is applied. The superpixels generated by applying ICTSLIC on keyframes are used to represent database videos. On other side, in online processing, ICTSLIC superpixel segmentation is applied on query frame and the superpixels generated by segmentation are used to represent query frame. Then videos similar to query frame are retrieved through matching done by calculation of Euclidean distance between superpixels of query frame and database keyframes. Results of the proposed method are irrespective of query frame features such as camera motion, object’s pose, orientation and motion due to the incorporation of ICTSLIC superpixels as base feature for matching and retrieval purpose. The proposed method is tested on the dataset comprising of different categories of video clips such as animations, serials, personal interviews, news, movies and songs which is publicly available. For evaluation, the proposed method randomly picks frames from database videos, instead of selecting keyframes as query frames. Experiments were conducted on the developed dataset and the performance is assessed with different parameters Precision, Recall, Jaccard Index, Accuracy and Specificity. The experimental results shown that the proposed method is performing better than the other state-of-art methods.

The Bag-of-visual-Words (BovW) model is one of the most popular visual content representation methods for large-scale content-based video retrieval. The visual words are quantized according to a visual vocabulary, which is generated by a visual features clustering process (e.g. K-means, GMM, etc). In principle, two types of errors can occur in the quantization process. They are referred to as the UnderQuantize and OverQuantize problems. The former causes ambiguities and often leads to false visual content matches, while the latter generates synonyms and may lead to missing true matches. Unlike most state-of-the-art research that concentrated on enhancing the BovW model by disambiguating the visual words, in this paper, we aim to address the OverQuantize problem by incorporating the similarity of spatial-temporal contexts associated to pair-wise visual words. The visual words with similar context and appearance are assumed to be synonyms. These synonyms in the initial visual vocabulary are then merged to rebuild a more compact and descriptive vocabulary. Our approach was evaluated on the TRECVID2002 and CC_WEB_VIDEO datasets for two typical Query-By-Example (QBE) video retrieval applications. Experimental results demonstrated substantial improvements in retrieval performance over the initial visual vocabulary generated by the BovW model. We also show that our approach can be utilized in combination with the state-of-the-art disambiguation method to further improve the performance of the QBE video retrieval.

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.

Pyramid regional graph representation learning for content-based video retrieval

With the vast growth and progress of multimedia on the Internet, particularly videos, which caused a growing demand for video retrieval, organizing, and automate systems, as many users mandate content-based retrieval systems over text-based retrieval systems. The process of extracting and selecting features, plays a very important role in content-based video retrieval regardless the significance of video characteristics. The reduction of time and space costs of the retrieval process is dependent on effective features selection. In this paper, we will discuss content-based video indexing and retrieval, in the application of video search engines CBVSE, which is the problem of searching for digital videos over the Internet. Addressing diverse approaches regarding video indexing and retrieval with brief outline and classification. Furthermore, we will propose a framework for a CBVSE that will offer a less complex solution for content-based video retrieval for video search engines over the web using reduced features vector with better accuracy to decrease the complexity trade-off. These features include temporal boundaries patterns combination and key-object concept features, as well as classifying the index list with a multi key-object concepts arrangement built on the statistical redundancy of the detected key-object concepts for each video shot in the sequence.

Merits and defects of note-based video retrieval and content-based video retrieval are investigated in this paper, and complementarity of two aspects each other are also discussed. A new theory of semantic processing in content-based video retrieval is explored. An object-oriented representation modeling of the video semantics, based on the multi-layers, is presented. And on this basis, a semantic extraction frame based on the multi-semantics is constructed.

An advanced sports video browsing and retrieval system based on multimodal analysis, SportsBR, is proposed in this work. Its main features include event-based sports video browsing and keyword-based sports video retrieval. The paper first defines the basic structure of our SportsBR system, and then introduces a novel approach that integrates multimodal analysis, such as visual streams analysis, speech recognition, speech signal processing and text extraction to realize event-based video clips selection. The experimental results for sports video of world cup football games indicate that multimodal analysis is effective for video browsing and retrieval by quickly browsing event-based video clips and inputting keywords according to a predefined sports vocabulary database. The system is proved to be helpful and effective for the overall understanding of the sports video content.

At present, content-based video retrieval (CBVR) is the most mainstream video retrieval method, using the video features of its own to perform automatic identification and retrieval. This method involves a key technology, i.e. shot segmentation. In this paper, the method of automatic video shot boundary detection with K-means clustering and improved adaptive dual threshold comparison is proposed. First, extract the visual features of every frame and divide them into two categories using K-means clustering algorithm, namely, one with significant change and one with no significant change. Then, as to the classification results, utilize the improved adaptive dual threshold comparison method to determine the abrupt as well as gradual shot boundaries.Finally, achieve automatic video shot boundary detection system.

A new unified video indexing and retrieval method is presented to classify temporal texture videos using spatial as well as temporal cooccurrence statistics of block-based motion vectors, so keeping the computational complexity for retrieval within a real-time bound. Experimental results clearly demonstrate the superiority of the proposed method over existing temporal cooccurrence matrix-based solutions.

Scene change detection is an essential step to automatic and content-based video indexing, retrieval, and browsing. In this paper, a robust scene change detection method is presented, which analyzes both audio and visual information sources and accounts for their inter-relations and coincidence to semantically identify video scenes. Audio analysis focuses on the segmentation of audio source into four types of semantic data such as silence, speech, music, and environmental sound. Speech data are further decomposed into different elements according to different speakers. Meanwhile, visual analysis partitions video source into shots. Results from single source segmentation are in some cases suboptimal. By combining visual and audio features, the scene extraction accuracy is enhanced, and more semantic segmentations are developed. Experimental results are proven to be appropriate for content-based video indexing and retrieval.

In the field of computer vision and image processing CBIR (Content based image retrieval) system become an active research area. The CBIR technique involves feature extraction based on image features (like color, shape and texture) and defining rules for comparing image. A classifier is also used by authors for image classification. For this purpose an image database taken from James Z. Wang research group website is used by many authors. Due to enormous increase in the size of database traditional methods of image retrieval by using keywords does not provide accurate result. Thus the concept of CBIR system is emerged. In last couple of years many method of feature extraction is developed but since now researcher are not able to develop a system which has gained high accuracy of human visual perception in distinguishing pictures. Therefore earlier researchers have concentrated to increase the retrieval accuracy of CBIR system by using different combination of features. The objective of this paper is to present review on a CBIR technique which combined four kinds of features set including every kind of feature for developing feature vector. Color based features (histogram), statistical features (mean, median and standard deviation), spatial features (Gabor feature and wavelet features) and texture features (GLCM) is used for forming feature vector. A SVM classifier is used with CBIR system to increase retrieval accuracy. SVM (support vector machine) is a machine learning classifier which provides good classification accuracy. Keywords: CBIR, SVM, NN, GLCM, HSV Color space, Euclidean distance matrix.

Video retrieval based on object discovery

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.

Content-based video retrieval is very interesting point where it can be used in our daily life. Video retrieval is regarded as one of the most important in multimedia research. The development of multimedia data types there is demand of video retrieval system. Video retrieval can be used for video search and browsing which are useful in web applications. Selection of extracted features play an important role in content based video retrieval. There are two types of feature extraction, low level feature extraction and high level feature extraction. Low level feature extraction based on color, shape, texture, spatial relationship. The main goal of this paper is that, user can give the two different types of input in the form of image query and the text query. First one is that give the input in the form of image query and retrieved image which is similar to the query image by using the CBIR algorithm. CBIR is still developing science. Retrieval of images based on visual features such as color, texture and shape. In this paper gives a detail description of a system developed for retrieving images similar to a query image from a different large set of image. Second one is that give the input in the form of text query and retrieved image by using the ABIR technique. ABIR is powerful algorithm for the information retrieval it can utilize their powerful natural language. Annotation is never complete.