Bag of Visual Words

Image representation using bag of visual words approach is commonly used in image classification. Features are extracted from images and clustered into a visual vocabulary. Images can then be represented as a normalized histogram of visual words similarly to textual documents represented as a weighted vector of terms. As a result, text categorization techniques are applicable to image classification. In this paper, our contribution is twofold. First, we propose a suitable Term-Frequency and Inverse Document Frequency weighting scheme to characterize the importance of visual words. Second, we present a method to fuse different bag-of-words obtained with different vocabularies. We show that using our tf.idf normalization and the fusion leads to better classification rates than other normalization methods, other fusion schemes or other approaches evaluated on the SIMPLIcity collection.

In this work, we compare the performances of four
different classifiers for detection of diabetic
retinopathy (DR) in retinal pathology images. The
classifiers considered are Naïve Bayes Classifier,
Decision Tree Classifier, Support Vector Machine
(SVM) Classifier, and Feed-forward Neural
Network Classifier. In the first step, the interest
points and descriptors are extracted from the fundus
images using the SURF algorithm. In the second
step, based on the extracted interest points, a Bagof-Visual-Words histogram is created for each
image. Finally in the third step, the classifiers are
first trained using the histograms of the images and
then they are employed to classify whether a retinal
image is normal or abnormal based on DR
anomalies. For conducting the experiments, two
well-known databases, namely, RetiDB and
Messidor are used.

Существует широкий круг задач, где требуется анализ, аудио-визуальных моделей реальности. В частности, для многих военных и гражданских приложений, необходимо наличие поиска нечетких дубликатов видео. Для мирного применения, — это группировка подсказок в выдаче поисковиков, поиск неправомерных публикаций видеофайлов, сокращение объема хранимой и передаваемой информации. Для военного применения можно тоже перечислить несколько вариантов.

— we present a new approach to classify breast tissue density which is widely accepted to be an important risk indicator for the development of breast cancer. The computer aided diagnosis (CAD) framework developed, first segments the breast area then represents each image using the bag-of-features (BoF) approach. To represent the images, we first extract local binary patterns (LBP) features, which are then quantized to create a codebook. Second, we encode the features using the obtained codebook and a sparse coding algorithm to obtain a final image representation. Finally, we use support vector machines (SVM) classifier to carry out the classification task. In order to evaluate the efficiency of the proposed approach, we tested the framework using the digital database of screening mammograms (DDSM). The results showed that 91.25% of the samples were correctly classified. We also investigated the codebook size and selected the one that enhanced the results. Our proposal showed better performance compared to previous methods that classified the same dataset. Keywords— breast tissue density classification; computer-aided diagnostic systems; bag of features; local binary patterns; sparse coding; LLC;max-pooling .

In this study we present an efficient image categorization and retrieval system applied to medical image databases, in particular large radiograph archives. The methodology is based on local patch representation of the image content, using a "bag of visual words" approach. We explore the effects of various parameters on system performance, and show best results using dense sampling of simple features with spatial content, and a nonlinear kernel-based support vector machine (SVM) classifier. In a recent international competition the system was ranked first in discriminating orientation and body regions in X-ray images. In addition to organ-level discrimination, we show an application to pathology-level categorization of chest X-ray data, the most popular examination in radiology. The system discriminates between healthy and pathological cases, and is also shown to successfully identify specific pathologies in a set of chest radiographs taken from a routine hospital examination. This is a first step towards similarity-based categorization, which has a major clinical implications for computer-assisted diagnostics.

Image representation is an important issue for medical image analysis, classification and retrieval. Recently, the bag of features approach has been proposed to classify natural scenes, using an analogy in which visual features are to images as words are to text documents. This process involves feature detection and description, construction of a visual vocabulary and image representation building through visualword occurrence analysis. This paper presents an evaluation of different representations obtained from the bag of features approach to classify histopathology images. The obtained image descriptors are processed using appropriate kernel functions for Support Vector Machines classifiers. This evaluation includes extensive experimentation of different strategies, and analyses the impact of each configuration in the classification result.

Plant leaves provide sufficient features to distinguish
them among other species. Identification of plants using
leaf images is a classic problem in digital image processing.
Usually those image processing systems use shape based digital
morphological features for leaf identification task. Even there are
number of studies on leaf based plant identification, very few of
them are for mobiles. In this paper we describe a leaf image based
plant identification system using SIFT features combining with
Bag Of Word (BOW) model and Support Vector Machine (SVM)
classifier. The system is trained to classify 20 species and obtained
96.48% accuracy level. Based on the results, we developed an
Android application communicates with the server and gives users
the ability to identify plant species using photographs taken of
plant leaves using the smart phone.

In this paper, we propose a multi-cue object representation for image classification using the standard bag-of-words model. Ever since the success of the bag-of-words model for image classification, several modifications of it have been proposed in the literature. These variants target to improve key aspects, such as efficient and compact dictionary learning, advanced image encoding techniques, pooling methods , and efficient kernels for the final classification step. In particular, " soft-encoding " methods such as sparse coding, locality constrained linear coding, Fisher vector encoding, have received great attention in the literature, to improve upon the " hard-assignment " obtained by vector quantization. Nevertheless, these methods come at a higher computational cost while little attention has been paid to the extracted local features. In contrast, we propose a novel multi-cue object representation for image classification using the simple vector quantization, and show highly competitive classification performance compared to state-of-the-art methods on popular datasets like Caltech-101 and MICC Flickr-101. Apart from the object representation, we also propose a novel keypoint detection scheme that helps to achieve a classification rate comparable to the popular dense keypoint sampling strategy, at a much lower computational cost.

The term “Near-duplicate” is an object that is fully or partly similar to another object. There are natural and artificial near-duplicates. Natural near-duplicates are similar objects within the similar environment, while artificial near-duplicates are objects, which comes from one original object. Now lets, turn to the main aims of the the near duplicate retrieval. Firstly, the near duplicate retrieval may be helpful for the optical navigation of cruise missiles or pilotless planes. Secondly, it is helpful for grouping snippets of the search engines. Moreover, it is necessary for the video classification and copyright management. This is the most important part for the online cinema. Nowadays there are several ways to find near duplicates. First one is based on global video features. Another is based on local and global features of particular frames. Then, youtube.com uses the audio based approach while, Licenzero uses the visual words. Also the combinations of different methods are applied. We propose an algorithm of the near duplicate video retrieval based on the shot detection. A shot is a sequence of the frames, which are considerably different form the frames of another sequence. Imagine our task as the following: There are a lot of the initial videos. Somebody gives us some new video, and we should say if it is a copy of one or more of the initial videos. We compute scene descriptors for every shot of every initial video and for every shot of the given video. Then we compare these descriptors to each other.If several descriptors of the given video are coincide with descriptors of the initial videos, it means these parts of videos are duplicates. Else, the given video is unique. As a result of this work we have the approach for the near-duplicate video retrieval. Firstly, It includes length ratios and relative lengths. Secondly, we use the scene aliment. The same method is used in the computer linguistics for the sentences aliment in the bilingual corpora. It is called Gale-Church algorithm. Thirdly, we propose scene descriptors. Several experiments have been conducted.

This paper presents a novel method for basal-cell carcinoma detection, which combines state-of-the-art methods for unsupervised feature learning (UFL) and bag of features (BOF) representation. BOF, which is a form of representation learning, has shown a good performance in automatic histopathology image classification. In BOF, patches are usually represented using descriptors such as Scale-Invariant Feature Transform (SIFT) and Discrete Cosine Transformation (DCT). We propose to use UFL to learn the patch representation itself. This is accomplished by applying a topographic UFL method (T-RICA), which automatically learns visual invariance properties of color, scale and rotation from an image collection. These learned features also reveals these visual properties associated to cancerous and healthy tissues and improves carcinoma detection results by 7% with respect to traditional autoencoders, and 6% with respect to standard DCT representations obtaining in average 92% in terms of F-score and 93% of balanced accuracy.

Having effective methods to access the desired images is essential nowadays with the availability of a huge amount of digital images. The proposed approach is based on an analogy between content-based image retrieval and text retrieval. The aim of the approach is to build a meaningful mid-level representation of images to be used later on for matching between a query image and other images in the desired database. The approach is based firstly on constructing different visual words using local patch extraction and fusion of descriptors. Secondly, we introduce a new method using multilayer pLSA to eliminate the noisiest words generated by the vocabulary building process. Thirdly, a new spatial weighting scheme is introduced that consists of weighting visual words according to the probability of each visual word to belong to each of the n Gaussian. Finally, we construct visual phrases from groups of visual words that are involved in strong association rules. Experimental results show that our approach outperforms the results of traditional image retrieval techniques.

Bag-of-Visual-words (BoV) approach to image classification is popular among computer vision scientists. The visual words come from the visual vocabulary which is constructed using the key points extracted from the image database. Unlike the natural language, the length of such vocabulary for image classification is
task dependent. The visual words capture the local invariant features of the image. The region of image over which a visual word is constrained forms the spatial content for the visual word. Spatial pyramid representation of images is an approach to handle spatial information. In this paper, we study the role of
vocabulary lengths for the levels of a simple two level spatial pyramid to perform binary classifications. Two binary classification problems namely to detect the presence of persons and cars are studied. Relevant images from PASCAL dataset are being used for the learning activities involved in this work

This paper proposes an adaptive image representation learning method for cervix cancer tumor detection. The method learns the representation in two stages, a local feature description using a sparse dictionary learning and a global image representation using a bag-of-features (BOF) approach. The resultant representation is thus a BOF histogram, learned from a sparse local patch representation. The parameters of the sparse learning representation algorithm are tuned up by searching dictionaries with low coherence and high sparsity. The proposed method was evaluated in a dataset of 394 cervical histology images with tumoral and non-tumoral pathologies acquired at a 10X magnification and a resolution of 3800 × 3000 pixels in RGB color. A conventional BOF image representation, using a linearized raw-block patch descriptor, was selected as the baseline. The preliminary results show that our proposed method improves the baseline for all different BOF dictionary sizes (125, 250, 500, 1000 and 2000). Under a 10 cross-validation test and a 2000 BOF dictionary, the best performance was 0.77 ± 0.04 in average accuracy, improving in 2.5% the baseline. These results suggest that a learning-from-data approach could be used in different stages of an image classifier construction pipeline, in particular for the image representation stage.

In this paper we propose a recognition system for classifying
NBI images of colorectal tumors into three types (A, B, and C3)
of structures of microvessels on the colorectal surface. These types have
a strong correlation with histologic diagnosis: hyperplasias (HP), tubular adenomas (TA), and carcinomas with massive submucosal invasion
(SM-m). Images are represented by Bag-of-features of the SIFT descriptors
densely sampled on a grid, and then classied by an SVM with an
RBF kernel. A dataset of 907 NBI images were used for experiments with
10-fold cross-validation, and recognition rate of 94.1% were obtained.

The Fisher kernel (FK) is a generic framework which combines the benefits of generative and discriminative approaches. In the context of image classification the FK was shown to extend the popular bag-of-visual-words (BOV) by going beyond count statistics. However, in practice, this enriched representation has not yet shown its superiority over the BOV. In the first part we show that with several well-motivated modifications over the original framework we can boost the accuracy of the FK. On PASCAL VOC 2007 we increase the Average ...

The Bag-of-Visual Word modelling of images has been recognised as an important step in the categorisation of images for Image retrieval due to its ability to support image semantic content detection. However, its application often leads to significant misclassifications due to its neglecting of the spatial locations of the visual words within the image space during the modelling process. This paper presents Visual sentences constructed via unsupervised Region of interest detection as a viable means of including the spatial locations of visual words in the Bag-of-Visual Word modelling, thereby eliminating the spatial incoherency often associated with the Bag-of-Visual Word Modelling.

the similarity or the distance measure have been used widely to calculate the similarity or dissimilarity between vector sequences, where the document images similarity is known as the domain that dealing with image information and both similarity/distance has been an important role for matching and pattern recognition. There are several types of similarity measure, we cover in this paper the survey of various distance measures used in the images matching and we explain the limitations associated with the existing distances. Then, we introduce the concept of the floating distance which describes the variation of the threshold's selection for each word in decision making process, based on a combination of Linear Regression and cosine distance. Experiments are carried out on a handwritten Arabic image documents of Gallica library. These experiments show that the proposed floating distance outperforms the traditional distance in word spotting system.

XI All-Russian Conference “Neurocomputers and their application”, Мoscow: MSUPE, 19.03.2013.

Понятие «нечеткий дубликат» означает неполное или частичное совпадение текущего документа (изображения) с другим документом подобного класса.

Дубликаты бывают естественные и искусственные. Естественные дубликаты — схожие объекты, при схожих условиях. Искусственные нечеткие дубликаты — полученные на основе одного и того же оригинала.

Поиск нечетких дубликатов может быть полезен для оптической навигации беспилотных аппаратов, для определения характера ландшафта местности, составления каталогов видео, группировки сниппетов поисковых систем, фильтрация видео рекламы, и поиска пиратского видео.

Нечеткие дубликаты тесно связаны с проблемами классификации видео и поиска по видео. Но эти задачи являются самостоятельными.

На данный момент нечеткие дубликаты пытаются искать, сравнивая глобальные особенности видео, глобальные и локальные особенности кадров и множеств кадров. Сравнивают звуковой ряд (так, например делюат в ютубе). Ищут и сравнивают визуальные слова, характерные последовательности (лицензЕро). Используется комбинация методов.

Мы предлагаем алгоритм поиска нечетких дубликатов на основе сцен. Есть множество исходных файлов, для видео сцен этих файлов вычислены дескрипторы сцен. Получаем новое видео. Требуется установить является ли новое видео дубликатом существующих. Вычисляем дескрипторы сцен этого видео . Дескриптор каждой сцены нового видео сравниваем с каждым дескриптором кадой сцены каждого исходного видео.

Если на нектором временном промежутке дескрипторы совпали, считаем, считаем, что временной промежуток является дубликатом одной из частей исходного видео. Если не несовпали ни на одном — считаем видео уникальным.

Что такое сцена?

Существует три различных понятия. Кадр, Фотографический кадр — статическая картинка. Сцена, монтажный кадр — множество кадров связанных единством места и времени. Съемка, кинематографических кадр, множество кадров связанных единством съемки. 1 сцена может включать несколько съемок. Съемку часто называют <<сценой>>. Далее мы будем рассматривать съемку, но называть ее будем <<сценой>>.

Введем формальное определение. Сцена-съемка, набор кадров определенной временной области кадры которой значительно отличаются от кадров соседних опластей. Аналогичным образом можно ввести понятие звуковой сцены, правда возникает отдельная проблема выделения звуковых-кадров.

Выделение сцен происходит на основе трех базовых подходов. Это сравнение гистограм соседних кадров. Сравнение спектров кадров и сравнение векторов оптического потока. На слайде приведены первые кадры сцен рекламы на МТС, выделенные c помощью ffmpeg. ffmpeg использует сравнение гистограмм кадров.

Если исходную рекламу сжать разными кодеками, мы получим нечеткие дубликаты этого видео. Выделяя сцены в каждом видео, увидим, что точки перемены сцен для этих двух файлов не совпадают.

Если взять более продолжительное видео, и выделить сцены при более высокой чувствительности, то можно заметить, что для разных кодеков сцены определяются совсем не однородно. Более того, некоторые сцены могут не распознаться.

Для решения этой проблемы мы предлагаем использовать относительные длины сцен. Для каждой сцены вычисляем отношение длины сцены к длинам остальных сцен. Для первых четрех сцен рекламы МТС получаем следующую матрицу. В практических задачах удобнее вычислять отношения длин, для трех предыдущих сцен. Это удобно и вслучае, если все видео целиком нам недоступно.

Если относительная длина сцены одного видео отличается от длины соответствующей сцены другого видео не более чем в два раза, то можно предположить, что сцены выражают одно и то же явление. В этом случае то длину меньшей сцены можно сложить с длиной следующей сцены этого же видео, и рассматривать объединенную сцену как одну. Подобные метод называют алгоритмом Гейла-Черча. Используется в математической лингвистике для выравнивания праллельных корпусов тесктов на разных языках.


Если относительные длины двух сцен совпали, не факт что сцены действительно одинаковы. Требуется сравнивать внутренние свойства сцен. Можно взять начальные и конечные кадры.

Сравнивать можно попиксельно, на основе детектора краев. В данном случчае удобно использовать глобальные дескриптор GIST, и мешок слов.

GIST представляем из себя простой но не очень точный способ глобального описания изображения. Применим для широкого круга задач.

Мешок слов, обычно более точен, при достаточном размере словаря. Необходимо, чтобы был набор изображений, на которых можно обучиться. Хорош, если работа ведется только в рамках конкретной предметной области. Но работает медленне и потенциально бесконечен по памяти.

Т.о. получили дескриптор сцены. Он состит из вектора отношений длины сцены к длинам других сцен; Его удобно сразу хранить, с объединениями соседних сцен учитывая гиппотезу Гейла Черча; И характеристики начального и конечного кадров. В зависимоти от задачи мешок слов или GIST.

Вернемся к предложеннуму алгоритмы. Мы сравнивали дескрипторы на прямую в пространстве L2. Но это не эффективно, для больших дескрипторов.

Можно предложить использование цифровых семантических подписей. Введем бинарные подписи. Подписи должны быть близки для близких дескипторах в L2.

Самое простое, что можно предложить: Линейно чувствительные хеши. Пространство дескрипторов, делим гиперплоскостью на 2 подпространства. Назначаем дескрипторам этих подпространств подпись 0 или 1. Получили бит подписи. С увеличением числа бить ассимптотически приближаем метрику L2. Для задач подобного рода лучше себя показали обучаемые хеши. К ним относятся бустинг и ограниченная машина Больцмана. В качесвтве бустинга исользуется модификация AdaBoost. Цель — получить такие подписи, расстояние между дескрипторами вычисляется, будет вычисляться как расстояние Хемминга.

Ограниченная машина Больцмана: Вероятностная рекурентная нейронная сеть. Вероятностная версия сети Хопфилда. Или нейросетевая версия Скрытой Модели Маркова. Применяется модификация, без связей внутри слоев. Мощность словем понижается от размера входного вектора до размера требуемого кода.


В результате проделанной работы: был разработан подход поиска нечетких дубликатов видео на основе сцен. Его основные моменты: относительные длины, выравнивания сцен на основе лингвистичского алгоритма Гейла-Черча, дескрипторы сцен; Проведен ряд экспериментов, которые показали удобства подхода.

Далее нам требуется разработать собственный инструмент для поиск перемены, сейчас используется ffmpeg. лучше проработать схемы с использованием Больцмана Провести эксперименты на реальном множестве исxодных данных.

Having effective methods to access the desired images is essential nowadays with the availability of huge amount of digital images. The proposed approach is based on an analogy between image retrieval containing desired objects (object-based image retrieval) and text retrieval. We propose a higher-level visual representation, for object-based image retrieval beyond visual appearances. The proposed visual representation improves the traditional part-based bag-of-words image representation, in two aspects. First, the approach strengthens the discrimination power of visual words by constructing an mid level descriptor, visual phrase, from frequently co-occurring and non noisy visual word-set in the same local context. Second, to bridge the visual appearance difference or to achieve better intra-class invariance power, the approach clusters visual words and phrases into visual sentence, based on their class probability distribution.

Recognizing human actions in realistic scenes has emerged as a challenging topic due to various aspects such as dynamic backgrounds. In this paper, we present a novel approach to taking audio context into account for better action recognition performance, since audio can provide strong evidence to certain actions such as phone-ringing to answer-phone. At first, classifiers are established for visual and audio modalities, respectively. Specifically, bag of visual-words model is employed to represent human actions in visual modality, a number of audio features are extracted for audio modality, and Support Vector Machine (SVM) is employed as the classification technique. Then, a decision fusion scheme is utilized to fuse classification results from two modalities. Since audio context is not always helpful, two simple yet effective decision rules are developed for selective fusion. Experimental results on the Hollywood Human Actions (HOHA) dataset demonstrate that the proposed approach can achieve better recognition performance than that of integrating scene context. Therefor, our work provides strong confidence to further explore how audio context influences realistic human action recognition.