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Content Based Photo Search
Dvořák, Pavel ; Beran, Vítězslav (referee) ; Španěl, Michal (advisor)
This thesis covers design and practical realization of a tool for quick search in large image databases, containing from tens to hundreds of thousands photos, based on image similarity. The proposed technique uses various methods of descriptor extraction, creation of Bag of Words dictionaries and methods of storing image data in PostgreSQL database. Further, experiments with the implemented software were carried out to evaluate the search time effectivity and scaling possibilities of the design solution.
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Content Based Photo Search
Bařinka, Radek ; Přibyl, Bronislav (referee) ; Španěl, Michal (advisor)
This thesis deals with the problematics of searching of photographs by the content and existing applications dealing with this subject. The aim is the local working application for searching of photographs by the content given by a pattern. The solution consists of the simple graphical interface, the support of saving data and the reading of data from the transferable local database. The application searches the photographs of a given set that are similar to the given pattern. The results are visually depicted to the user. Feature extraction and detection by photo content is solved by means SURF algorithm, visual vocabulary created by method k-means and a description of photography as a bag of words. In addition,the searching of photographs by cosine similarity of vectors enriched with the independent calculation of homography and the selection of regions searched in an example photography. At the end of the technical report the results of testing are presented.
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Image Database Query by Example
Dobrotka, Matúš ; Hradiš, Michal (referee) ; Veľas, Martin (advisor)
This thesis deals with content-based image retrieval. The objective of the thesis is to develop an application, which will compare different approaches of image retrieval. First basic approach consists of keypoints detection, local features extraction and creating a visual vocabulary by clustering algorithm - k-means. Using this visual vocabulary is computed histogram of occurrence count of visual words - Bag of Words (BoW), which globally represents an image. After applying an appropriate metrics, it follows finding similar images. Second approach uses deep convolutional neural networks (DCNN) to extract feature vectors. These vectors are used to create a visual vocabulary, which is used to calculate BoW. Next procedure is then similar to the first approach. Third approach uses extracted vectors from DCNN as BoW vectors. It is followed by applying an appropriate metrics and finding similar images. The conclusion describes mentioned approaches, experiments and the final evaluation.
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Machine Learning Concepts for Categorization of Objects in Images
Hubený, Marek ; Honec, Peter (referee) ; Horák, Karel (advisor)
This work is focused on objects and scenes recognition using machine learning and computer vision tools. Before the solution of this problem has been studied basic phases of the machine learning concept and statistical models with accent on their division into discriminative and generative method. Further, the Bag-of-words method and its modification have been investigated and described. In the practical part of this work, the implementation of the Bag-of-words method with the SVM classifier was created in the Matlab environment and the model was tested on various sets of publicly available images.
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Automatic Photography Categorization
Veľas, Martin ; Beran, Vítězslav (referee) ; Španěl, Michal (advisor)
This thesis deals with content based automatic photo categorization. The aim of the work is to create an application, which is would be able to achieve sufficient precision and computation speed of categorization. Basic solution involves detection of interesting points, extraction of feature vectors, creation of visual codebook by clustering, using k-means algorithm and representing visual codebook by k-dimensional tree. Photography is represented by bag of words - histogram of presence of visual words in a particular photo. Support vector machines (SVM) was used in role of classifier. Afterwards the basic solution is enhanced by dividing picture into cells, which are processed separately, computing color correlograms for advanced image description, extraction of feature vectors in opponent color space and soft assignment of visual words to extracted feature vectors. The end of this thesis concerns to experiments of of above mentioned techniques and evaluation of the results of image categorization on their usage.
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Object Instance Search in Image Dataset
Medvec, Juraj ; Brejcha, Jan (referee) ; Beran, Vítězslav (advisor)
This project is focused on the selected techniques for image searching. In the introduction are presented all existing solutions, which are used in daily use. The other chapters are focused on creating resulting framework. On the end is introduced the demo application, which is using the framework for searching books by front page image.
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Automatic Content-Based Image Categorization
Němec, Ladislav ; Španěl, Michal (referee) ; Veľas, Martin (advisor)
This thesis deals with automatic content-based image classification. The main goal of this work is implementation of application which is able to perform this task automatically. The solution consists of variable system using local image features extraction and visual vocabulary built by k-means method. Bag Of Words representation is used as a global feature describing each image. Support Vector Machines - the final component of this system - perform the classification based on this representation. In the last chapter, the results of this experimental system are presented.
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Photo Instead of QR Code
Štol, Jakub ; Španěl, Michal (referee) ; Beran, Vítězslav (advisor)
This thesis focuses on create system, which according to user's photo search the most similar photo in dataset a display additional information about this photo such as photo content, place and author. The system is primarily focused on fields, where the user wants to use this system and get information, which he want such as museums or galleries. Theoretical part focuses on concept system according to methods of image processing and searching image in the dataset. Practical part focuses on concept creating web application, which display more information about the photo via web interface.
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Automatic Selection of Representative Pictures
Bank, Tomáš ; Beran, Vítězslav (referee) ; Polok, Lukáš (advisor)
This paper belongs to field of computer vision. It deals with clustering photographs by content and selection of representative one. In this paper is described a few methods and approaches to reach the goal, and proposal of algorithm comes from those approaches. The example of usage this application can be selecting a representative photo from large albums.
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Automatic Photography Categorization
Veľas, Martin ; Řezníček, Ivo (referee) ; Španěl, Michal (advisor)
This thesis deals with content based automatic photo categorization. The aim of the work is to experiment with advanced techniques of image represenatation and to create a classifier which is able to process large image dataset with sufficient accuracy and computation speed. A traditional solution based on using visual codebooks is enhanced by computing color features, soft assignment of visual words to extracted feature vectors, usage of image segmentation in process of visual codebook creation and dividing picture into cells. These cells are processed separately. Linear SVM classifier with explicit data embeding is used for its efficiency. Finally, results of experiments with above mentioned techniques of the image categorization are discussed.
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