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Fused Image Based Video Search Engine

Fused Image Based Video Search Engine

Fused image based video search engine has been implemented using a simulation tool MATLAB version R2017a and the performance of the proposed system is analyzed using evaluation metrics including precision and recall. The experiments have been performed on a dataset of 40 videos. The collected videos contain the categories such as plane, ocean, street and mountains and the length of these videos is restricted to 150 to 250 frames. Some of the results obtained on a part of a video sequence utilized for retrieval are shown. Fig 3(a) shows one input video from the video database and Fig 3(b) shows the fused image created from the extracted key-frames of that sample video. The result of RGB fused image to HSV color space conversion for color moments is shown in Fig. 4 and RGB fused image to YC b C r
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Implementation of Android Based Mobile Phone Search Engine and Live Image Sender

Implementation of Android Based Mobile Phone Search Engine and Live Image Sender

Mobile phone crawler pass through a filter and filter pages which not changed from the time when last crawling happened. Mobile phone crawler presents most effective and talented searching, that type of crawler based on android java environment. By using that type of mobile crawler procedure, it has less important searches compare to other search engines. By this crawler system presentation improved, reason behind of this is those pages which are not customized and not repossess, along with this near photocopy recognition feature adds more privilege to reduce unwanted downloads. Page updating activity will presumed by crawler revisit frequency. That’s why mobile phone crawler will saved CPU cycle and decrease the traffic on the web.
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An Efficient Video Search Engine

An Efficient Video Search Engine

David Lowe proposed Scale Invariant Feature Transform which is a digital image descriptor for matching and recognition of digital image. The SIFT descriptors are of 128 descriptor vectors which are mainly used in point matching between different views of a 3- D scene and object recognition in computer vision. These descriptor are robust to rotation, scaling, translation transformation in the image and also robust to illumination variations. Therefore SIFT are useful for image matching and recognition under real world environment.

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Review on Capturing User Intention from Content Base Image Retrieval

Review on Capturing User Intention from Content Base Image Retrieval

Image searching techniques can be largely classified in to two types Text-Based Image Retrieval (TBIR) and Content-Based Image Retrieval (CBIR). Text-Based Image Retrieval (TBIR) uses text descriptions to get back appropriate images based on Time, location, events, and objects. Users type question keywords in the expect of finding a certain type of images. The search engine returns thousands of images ranked by the keywords extracted from the surrounding text. It is well known that text-based image search suffers from the ambiguity of query keywords. The keywords provided by users tend to be tiny. They cannot describe the content of images perfectly. The search results are noisy and consist of images with quite different semantic meanings. For example, if a user wants to search for an “apple” image, he/she may request a query look for using the keyword “apple” to the corresponding image search engine. The meanings of the word “apple” include apple fruit, apple computer, and apple iPod. The search results will contain different categories, such as “green apple,” “red apple,” “apple logo,” and “iPhone” because of the ambiguity of the word “apple”. This leads to ambiguous & noisy search results which are not adequate to fulfil the user query request.
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Analysing Creative Image Search Information Needs

Analysing Creative Image Search Information Needs

All examined systems are based on keyword search supporting Boolean and phrase searching, with most systems supporting predictive search. Stock libraries are based on controlled vocabularies and reduce search ambiguity by clarifying search term meaning (e.g. “orange” – colour, fruit, telecom company, city in Texas), photo-sharing network search operates on user-generated tags and file metadata, while image search systems also take into consideration text surrounding the image (i.e. content of a webpage containing the image). Although ranking algorithms are different in all five systems, in general results may be sorted by date, relevancy and popularity. The examined search engine and one of the photo-sharing networks contain restriction functionality known as “Safe Search” that prevent adult content from appearing in your search results. The algorithm is based on a number of factors, including keywords, links, and image content. One of the stock libraries also has “Exclude nudity” feature. The search engine and stock image libraries allow search of similar images, however, the underlying algorithm differs. In case of the search engine, it is content-based search based on colour-, texture-, and shape- histograms, while in case of stock libraries it is a description-based search. There are a number of content-based functionalities provided both by the examined search systems, i.e. search by colour, face detection, CopySpace™. Most systems support refiners of bibliographic and physical features: file type, source, size, timestamp and geolocation. Moreover, all systems allow filtering by licence type (Right Managed, Royalty Free, Creative Commons). As ISS1, ISS4, ISS5 are not designed to sell images, they do not have e-commerce mechanisms. In contrast ISS2 and ISS3 allow their users to specify a number of criteria (i.e. license type, format, duration and territory of use, industry, exclusivity) that define the final price of an image. ISS3 also allows limits on price range, which are not stated explicitly, but presented in a form of schematic price range from lowest price level (one dot) to the highest price level (four dots).
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Design of Colour and Texture Based Relevant Image Search Engine

Design of Colour and Texture Based Relevant Image Search Engine

Abstract— Online Navigation behavior grows each passing day, due to the interest of people in digital images is growing day by day; so the Users in many professional fields are exploiting the opportunities offered by the ability to access and manipulate remotely-stored images in all kinds of new and exciting ways but extracting information intelligently from a large image database, is a difficult issue and most commonly the irrelevant data’s which are not related to our search will be retrieved. This paper approaches a new method, to achieve high efficiency and effectiveness of Content-Based-Image-Retrieval in large scale image data. We achieve effectiveness via extracting the colour and texture features by combining colour feature and Discrete Wavelet Transform (DWT) as well as Color Correction Matrix (CCM) separately depending on the former, image retrieval based on multi feature fusion is achieved by using normalized Euclidean distance classifier. In terms of efficiency we propose a new method DWT to learn online user behavior. This technology used in medical, Photoshop and web field.
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SEO - SMO- SEM - PPC - VSEO

SEO - SMO- SEM - PPC - VSEO

Search engine optimization (SEO) is the process of affecting the visibility of a website or a web page in a search engine's "natural" or un-paid ("organic") search results. In general, the earlier (or higher ranked on the search results page), and more frequently a site appears in the search results list, the more visitors it will receive from the search engine's users. SEO may target different kinds of search, including image search, local search, video search, academic search, news search and industry-specific vertical search engines. Ex: SEO Training Institute in Hyderabad when entered by the user
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SEMANTIC SIGNATURE BASED IMAGE SEARCH IN WEB

SEMANTIC SIGNATURE BASED IMAGE SEARCH IN WEB

Recall we feed the same text query into a web text search engine to obtain top 200 documents when we estimate the co-occurrence probability of the word w and the query Q in Equation 8. These 200 documents are supposed to highly relate to the text query, and words occur in these docu-ments should be very much related to the query. The same idea with a different name called pseudo relevance feed-back has been proposed and shown performance improve-ment for text retrieval [22]. Since no humans are involved in the feedback loop, it is a “pseudo” feedback by blindly assuming top 200 documents and relevant. The relevance model estimates the co-occurrence probability from these documents, and then re-ranks the documents associated the images. The relevance model acquires many terms that are semantics related the query words, which in fact equals to query expansion, a technique widly used in Information Re-trieval community. By adding more related terms in the query, the system is expected to retrieve more relevant doc-uments, which is similar to use relevance model to re-rank the documents. For example, it may be hard to judge the relevance of the document using single query word “fish”, but it will become easier if we take terms such as “marine”, “aquarium”, “seafood”, “salmon” into consideration, and implicitly images in the page with many fish-realted terms should be more likely to be real fish. The best thing about relevance model is that it is learned automatically from doc-uments on the Internet, and we do not need to prepare any training documents.
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An Attribute-Assisted Re ranking Model for Web Image Search

An Attribute-Assisted Re ranking Model for Web Image Search

Image search reranking is an effective approach to refine the text-based image search result. Text-based image retrieval suffers from essential difficulties that are caused mainly by the incapability of the associated text to appropriately describe the image content. In this paper, reranking methods are suggested address this problem in scalable fashion. Based on the classifiers for all the predefined attributes, each image is represented by an attribute feature consisting of the responses from these classifiers. A hypergraph can be used to model the relationship between images by integrating low-level visual features and attribute features. Hypergraph ranking is then performed to order the images. Its basic principle is that visually similar images should have similar ranking scores. It improves the performance over the text- based image search engine. Keywords: hypergraph, attribute-assisted
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Shape Based Image Retrieval Using Fused Features

Shape Based Image Retrieval Using Fused Features

The shape is one of the essential elements of an image and plays a very important role in content-based search methods. Compared to texture or colour, the entire object can be represented by a shape, but at the same point, it requires a number of parameters to be represented explicitly. A better representation of the shape should be compact and retain the most important features of an object and the main requirements are compact features, improved accuracy of recovery, general application, low computational time and robustness of noise for good representation. By comparing and storing the understandable representation that major transition information can be achieved by representing the smart shape descriptor. Consequently, finding efficient and meaningful descriptors of the shape is a challenge in the recognition and retrieval of shape. To address these issues, we propose image retrieval based on shape using
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Clustering and Retrieval of Video Using Speech and Text Information

Clustering and Retrieval of Video Using Speech and Text Information

Abstract— For E-lecturing process, lecture videos are becoming more and more popular. For easy learning, students prefer e-learning procedure as it is properly understandable method. But most of the search engines search lecture videos according to video title. To perform most appropriate search, it’s a need to develop a content based video search system. This paper presents a content based video retrieval system. To develop a system, firstly we have to fragment a whole video into small frames to separate out textual data and audio data from it. This process is implemented with algorithm called as optical character recognition algorithm (OCR) and for recognizing and translating sound into textual metadata Automatic Speech Recognition algorithm is used. To improve relevancy of search results of OCR and ASR algorithms genetic algorithm is implemented in proposed system. Genetic algorithm is used for improving accuracy of results as it is applied on search query as well as at the time of video uploading. In our approach, user can perform a video search by giving search query as input in three formats: search query in text format, search query in image format, search query in audio clip and small video clip format.
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IJCSMC, Vol. 6, Issue. 7, July 2017, pg.128 – 137 A Study on Web Images Retrieval Using Content Based Image Retrieval Methods

IJCSMC, Vol. 6, Issue. 7, July 2017, pg.128 – 137 A Study on Web Images Retrieval Using Content Based Image Retrieval Methods

Re ranking system is improving the text based image retrieval methods.Re-ranking based on the visual The most search engines is using the text-based image retrieval as textual information is sometimes retrieved is noise images and irrelevant images are retrieved. This problem is reduced using re ranking images represents to their visual information’s.re ranking system is improving similarity images which considerations of an images similarity and diversity images that is effective and efficient system. Such as 1.semantic re ranking 2.tag based re-ranking 3.visual re- ranking 4.Bayesian re-ranking5.contextual re ranking 6. RL Sim re-ranking models so on. Visual re-ranking is combined textual and visual cues. Bayesian re-ranking is drives the best re-ranking images by maximizing visual information while minimizing irrelevant images reduced.re ranking is ordering the initial ranked list based on visual pattern that is called contextual based video and image search re-ranking.
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Visual Image Search

Visual Image Search

Now days, most of the Web search engines work on the principle of “query by words”. When the user provides keyword e.g. tiger, then the search engine provides results based on the textual information associated like file name, URL etc. This kind of scenario is appropriate when user search for documents, but same is not useful when user search for images. The textual information associated with the images is quite insufficient to represent semantic contents of the images. Also, there is mismatch between images and their associated textual information. Due to this problem, whenever user search for particular keyword, it is highly possible that the search engine will return the results that contain irrelevant images that the user is not expecting. The image for “tiger” mistakenly taken as relevant only due to the similarity in associated text in “tiger” though it is irrelevant. The same query words may refer to images that are semantically different, e.g., we cannot differentiate an animal tiger image from an image for a person whose name is Tiger, just with the text word “tiger”.
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Automated neurosurgical video segmentation and retrieval system

Automated neurosurgical video segmentation and retrieval system

Medical video repositories play important roles for many health-related issues such as medical imaging, medical research and education, medical diagnostics and training of medical professionals. Due to the in- creasing availability of the digital video data, index- ing, annotating and the retrieval of the information are crucial. Since performing these processes are both computationally expensive and time consuming, automated systems are needed. In this paper, we pre- sent a medical video segmentation and retrieval re- search initiative. We describe the key components of the system including video segmentation engine, im- age retrieval engine and image quality assessment module. The aim of this research is to provide an online tool for indexing, browsing and retrieving the neurosurgical videotapes. This tool will allow people to retrieve the specific information in a long video tape they are interested in instead of looking through the entire content.
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Frame based Video Retrieval using Video Signatures

Frame based Video Retrieval using Video Signatures

The World Wide Web today has grown so wide and the video-on-demand applications and video share web are becoming very popular day-by-day on the World Wide Web. An efficient video similarity search algorithm for content- based video retrieval is important in video-on-demand based services. However, there is no satisfying video similarity search algorithm showing cent percentage performance. It is proposed here to implement a video similarity measure algorithm based on the color-features of each video represented by a compact fixed size representation known as Video Signature. This Video signature which is based on the image signature is computed on the basis of YCbCr Histogram and the sum of its weighted means. The video signatures of videos are then used to find the similar videos in-terms of visually similar frames, by using the range. This method of similarity measure is assumed to be efficient in various aspects.
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Video Event Classification Based on Image Search Engine

Video Event Classification Based on Image Search Engine

A term frequency vector is made for every v ideo transcript to pick those with ma xima l range of inc idence to be used for annotation. the matter here is however similar those searched videos would be. The results of annotation is extre me ly enthusiastic about the similarity between videos and, it's unlikely to induce fu ll length annotated videos terribly the same as the input video. compared with transcripts, subtitles area unit a lot of out there in documentaries and films. A fra mework for c luster primarily {based} image retrieval and video annotation is projected in [14] that uses region based wave remodel simila rity methodology to match question video with preannotated videos. A video frame is split into 4X4 region and a feature vector is taken into acco unt victimization the center of mass purpose of the wave reworked in formation at the region. Once the feature vector is built, similarity is calcu lated victimization earth move ment distance live between identica l bloc ks of fra mes of every video.
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Review on Content Based Image Retrieval Reliance on User Intention

Review on Content Based Image Retrieval Reliance on User Intention

Abstract—The search engine returns thousands of images ranked by the keywords extracted from the surrounding text. It is well known that text-based image search suffers from the ambiguity of query keywords. The keywords provided by users tend to be short. They cannot describe the content of images accurately. The search results are noisy and consist of images with quite different semantic meanings. For example, if a user wants to search for an “apple” image, he/she may request a query search using the keyword “apple” to the corresponding image search engine. The meanings of the word “apple” include apple fruit, apple computer, and apple ipod. The search results will contain different categories, such as “green apple,” “apple,” “apple logo,” and “iphone” because of the ambiguity of the word “apple”. This leads to ambiguous & noisy search results which are not satisfactory to fulfil the user query request. In order to solve the ambiguity, additional information has to be used to capture users’ search intention.
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A Review-Image and Video Search Engine with Re-Ranking and Recommendation

A Review-Image and Video Search Engine with Re-Ranking and Recommendation

Picture and picture and recordings re-arranging as an issue philosophy to overhaul the inevitable results of electronic picture and picture and video search for, has been gotten a handle on by power business web request instruments. By requesting that the client pick a solicitation picture and picture and video from the pool, the remaining pictures are re-arranged concentrated around their visual likenesses with the request picture and video. Given a request authoritative word a pool of picture and recordings is atinitially recovered by the web record concentrated around printed data. A basic test is that
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A Review: Video Event Classification Based on Image Search Engine

A Review: Video Event Classification Based on Image Search Engine

making utilization of a novel logical data, movement design. At the point when subjects move around in the Field Of View (FOV) of a camera, movement estimations of human body are at the same time caught by two distinctive detecting strategies, including camera and PDAs outfitted with inertial sensors. At that point grouping models are prepared to perceive movement design from crude movement information. To distinguish the subject that showed up in video from the camera, a metric of separation is characterized to quantitatively gauge the likeness between movement succession perceived from video and each of those from advanced mobile phones. The errand of individual distinguishing proof is viably refined by correlation of movement groupings with movement sort as a side advantage for video explanation. Be that as it may, the technique has its cutoff points. To begin with, the selection of advanced mobile phones may break the subtle element of camera detecting. Subjects needs to convey advanced mobile phones with a specific end goal to be distinguished. Second, at present stand out subject was permitted in the camera FOV. Later on, we plan to make sense of powerful human location and following strategies to all the while recognize different subjects in camera video.
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MULTIMEDIA SEARCH ENGINE ON THE CONCEPT OF SEARCH BY EXAMPLE USING FOURIER TRANSFORMATION

MULTIMEDIA SEARCH ENGINE ON THE CONCEPT OF SEARCH BY EXAMPLE USING FOURIER TRANSFORMATION

and conferences dedicated to survey of more basic advancements and open issues in this area. They give the dynamic nature of the applications and research .With the based on this, we focus on this several major emerging trends in research, as well as standards related to general field of the multimedia search and retrieval. In that main purpose is to present some representative source. The addresses the promising direction in the integrating multimedia features in the extracting the syntactic and semantic structures in video or the images. It introduces some specific techniques (i.e., news, picture) depends on the client requirement which information in examining content by multiple levels. Focuses at the complementary direction in which the visual objects and their features are analyzed and indexed in the comprehensive way. These approaches result in search that allows users/client make direct manipulation visual content to form multimedia search the queries. The new directions are shows the incorporating knowledge from the machine learning and interactive systems to break the limit of decoding semantics from multimedia search engine content. Two main complementary approaches are shows here probabilistic graphic model and video and, finally the covers an important trend in the multimedia content description standard, MPEG-7, and its impact on several applications such as interoperable meta search environment with the Fourier transformation.
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