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    Visual Word: Unlocking the Power of Image Understanding
    Visual Word: Unlocking the Power of Image Understanding
    Visual Word: Unlocking the Power of Image Understanding
    Ebook130 pages1 hour

    Visual Word: Unlocking the Power of Image Understanding

    By Fouad Sabry

    ()

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    About this ebook

    What is Visual Word


    Visual words, as used in image retrieval systems, refer to small parts of an image that carry some kind of information related to the features or changes occurring in the pixels such as the filtering, low-level feature descriptors.


    How you will benefit


    (I) Insights, and validations about the following topics:


    Chapter 1: Visual Word


    Chapter 2: Code


    Chapter 3: Information retrieval


    Chapter 4: Image segmentation


    Chapter 5: Automatic summarization


    Chapter 6: Latent semantic analysis


    Chapter 7: Content-based image retrieval


    Chapter 8: N-gram


    Chapter 9: Document-term matrix


    Chapter 10: Full-text search


    (II) Answering the public top questions about visual word.


    (III) Real world examples for the usage of visual word in many fields.


    Who this book is for


    Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of Visual Word.

    LanguageEnglish
    PublisherOne Billion Knowledgeable
    Release dateMay 4, 2024
    Visual Word: Unlocking the Power of Image Understanding

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      Book preview

      Visual Word - Fouad Sabry

      Chapter 1: Visual Word

      Visual words, as employed in image retrieval systems, refer to short portions of an image that hold information about the features (such as the color, shape, or texture) or changes in the pixels, such as the filtering, low-level feature descriptors (SIFT or SURF).

      Text retrieval system (or information retrieval system) methodologies

      Consider that the pixels of an image, which are the tiniest portions of a digital image and cannot be divided further, are similar to the alphabetical letters of a language. Then, a group of pixels within an image (a patch or arrays of pixels) constitutes a word. Then, each word can be reprocessed within a morphological system to retrieve a related term. Then, multiple words with the same meaning will refer to the same concept (like in any language). Numerous words share the same meaning and constitute the same phrase (have the same information). According to this perspective, researchers can adapt text retrieval techniques to picture retrieval systems.

      This approach can be applied to games in order to determine which words and phrases will appear in our visuals. The objective is to attempt to comprehend the images using a vocabulary of visual words..

      A small region of a picture that can include any information in any feature space, such as color or texture changes.

      Generally speaking, visual words (VWs) exist in a feature space of continuous values, implying a vast number of words and, consequently, a vast language. Since image retrieval systems must use text retrieval techniques depending on natural languages, which have a limit on the number of terms and words, the number of visual words must be reduced.

      There are numerous ways to overcome this issue, such as partitioning the feature space into ranges with shared features (which can be considered as the same word). However, this technique has numerous flaws, including the division strategy and the breadth of the range in the feature space. Using a clustering method to classify and merge words conveying common information into a finite number of terms is another solution presented by researchers.

      The consequence of clustering in the feature space (centers of the clusters). Multiple patches can provide the closest information in feature space, thus we can consider them equivalent.

      As the Term in a text (the infinity verb, nouns, and articles) refers to numerous common words with the same properties, the visual term (with its clustering result) will refer to all common words that shared the same information in a feature space.

      Lastly, if all images correspond to the same set of visual concepts, then they can all communicate in the same language (or visual language).

      A collection of visual words and phrases.

      Considering the visual terms alone is the Visual Vocabulary which will be the reference and retrieval system that will depend on it for retrieving images.

      This visual language will represent all images as a collection of visual words, or bag of visual words.

      A collection of visual words that together explain the meaning of a portion or the entire image.

      On the basis of this type of picture representation, it is possible to create an image retrieval system using text retrieval techniques. Nevertheless, because all text retrieval systems rely on terms, the user's query images must be transformed into a collection of visual words within the system. The system will then compare these visual terms with every visual term in the database.

      {End Chapter 1}

      Chapter 2: Code

      For the purposes of communication and information processing, a code is a set of principles that transforms information—such as a letter, word, sound, image, or gesture—into another form, sometimes shorter or secret, for storage on a storage device or for transmission over a communication channel. An early example is the development of language, which allowed people to express verbally what they were thinking, seeing, hearing, or feeling to others. However, speaking restricts the audience to those present at the time the speech is delivered and limits the range of communication to the distance a voice may travel. The advent of writing, which transformed verbal communication into visual symbols, increased the potential for communication over time and distance.

      Encoding is the process of transforming data from a source into symbols for transmission or storage. The opposite procedure, known as decoding, involves translating code symbols into a language that the recipient may comprehend, such as English or/and Spanish.

      Coding is used to facilitate communication in situations when it would be difficult or impossible to do so using regular plain language, either verbally or in writing. For instance, semaphore encrypts portions of the message, generally single characters and numbers, using the arrangement of flags held by the signaler or the arms of the semaphore tower. The flags can be read by someone far away, and they can repeat the messages sent.

      A code is typically thought of in information theory and computer science as a method that discretely represents symbols from a source alphabet by encoded strings, which could be in a different target alphabet. Concatenating the encoded strings yields an extension of the code for encoding sequences of symbols across the source alphabet.

      This is a small example before providing a definition that is mathematically exact. The diagram

      C = \{\, a\mapsto 0, b\mapsto 01, c\mapsto 011\,\}

      the code, whose source alphabet is the set \{a,b,c\} and whose target alphabet is the set \{0,1\} .

      Using the code's extension, the encoded string 0011001 can be grouped into codewords as 0 011 0 01, and these in turn can be used to decode the original symbols' order, acab.

      Making use of concepts from formal language theory, The following is a detailed mathematical definition of this idea: S and T should be two finite sets, alphabets known as the source and target, respectively.

      A code C:\, S \to T^* is a total function mapping each symbol from S to a sequence of symbols over T.

      The extension C' of C , is a homomorphism of S^{*} into T^{*} , It automatically converts every set of source symbols into a set of target symbols.

      In this section, we'll talk about codes that translate each source (clear text) character into a code word taken from a dictionary, which when concatenated yields an encoded string. When clear text characters have varied probabilities, variable-length codes are extremely helpful; see also entropy encoding.

      A prefix code is one that possesses the property known as the prefix property: no other valid code word in the set has a prefix (start) that is also a valid code word in the system. The most well-known algorithm for generating prefix codes is huffman coding. Even when the prefix code was not generated by a Huffman method, it is frequently referred to as Huffman codes. The country and publisher sections of ISBNs, country calling numbers, and the Secondary Synchronization Codes used by the UMTS WCDMA 3G Wireless Standard are additional instances of prefix codes.

      The possible sets of codeword lengths in a prefix code are described by Kraft's inequality. Almost any one-to-many code that can be uniquely decoded, not just prefix codes, must satisfy Kraft's inequality.

      Additionally, codes can be employed to represent data in a way that is more resilient to transmission or storage failures. The way that this supposedly error-correcting code functions is by carefully constructing redundancy into the stored (or transmitted) data. Examples include the space-time codes, low-density parity-check codes, Reed-Solomon, Reed-Muller, Walsh-Hadamard, Bose-Chaudhuri-Hochquenghem, Turbo, Golay, and Goppa. Error-detecting algorithms can be improved to find random or burst errors.

      By substituting shorter words for words like ship or invoice, a cable code enables the same information to be communicated with fewer characters, more rapidly, and for

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