Sequential Hierarchical Image Recognition Based on the Pyramid Histograms of Oriented Gradients with Small Samples

A. Savchenko; N. S. Belova; Milov V.

doi:10.1007/978-3-319-26123-2_2

Publications

?

Sequential Hierarchical Image Recognition Based on the Pyramid Histograms of Oriented Gradients with Small Samples

Ch. 2. P. 14–23.

Savchenko A., Belova N. S., Milov V.

In this paper we explore an application of the pyramid HOG (Histograms of Oriented Gradients) features in image recognition problem with small samples. A sequential analysis is used to improve the performance of hierarchical methods. We propose to process the next, more detailed level of pyramid only if the decision at the current level is unreliable. The Chow’s reject option of comparison of the posterior probability with a fixed threshold is used to verify recognition reliability. The posterior probability is estimated for the homogeneity-testing probabilistic neural network classifier on the basis of its relation with the Bayesian decision. Experimental results in face recognition are presented. It is shown that the proposed approach allows to increase the recognition performance in 2–4 times in comparison with conventional classification of pyramid HOGs.

Language: English

DOI

Text on another site

Keywords: распознавание изображений Image recognition probabilistic neural network вероятностная нейронная сеть sequential analysis статистический последовательный анализ иерархическая классификация hierarchical recognition

In book

Analysis of Images, Social Networks and Texts. 4th International Conference, AIST 2015, Yekaterinburg, Russia, April 9–11, 2015, Revised Selected Papers

Vol. 542: Series: Communications in Computer and Information Science. , Switzerland: Springer, 2015.

Dish-ID: A neural-based method for ingredient extraction and further recipe suggestion.

Shchuka I., Miftakhov S., Patrushev V. et al., , in: 2020 International Conference Engineering and Telecommunication (En&T).: IEEE, 2020. P. 1–5.

The paper presents a method for meal recognition, ingredient extraction and recipe suggestion in the Russian language. The proposed algorithm consists of several consecutive stages. On the first stage the model extracts a list of ingredients from a photo of the dish, based on which recipes on the second stage are selected. Two ingredient extraction ...

Added: June 14, 2026

Astrocytes mediate analogous memory in a multi-layer neuron–astrocyte network

Tsybina Y., Kastalskiy I., Krivonosov M. et al., Neural Computing and Applications 2023 Vol. 34 No. 11 P. 9147 –9160

Modeling the neuronal processes underlying short-term working memory remains the focus of many theoretical studies in neuroscience. In this paper, we propose a mathematical model of a spiking neural network (SNN) which simulates the way a fragment of information is maintained as a robust activity pattern for several seconds and the way it completely disappears ...

Added: April 9, 2025

Three-way classification for sequences of observations

A. V. Savchenko, L. V. Savchenko, Information Sciences 2023 Vol. 648 Article 119540

This article introduces the novel technique to reduce the computation time for classifying a sequence of observations (frames), such as a video stream, where each observation is described by high-dimensional embeddings extracted by a deep neural network. By using the methodology of granular computing, an observed sequence is represented at various scales using different frame ...

Added: August 27, 2023

Effective face recognition based on anomaly image detection and sequential analysis of neural descriptors

Sokolova A., Savchenko A., , in: 2023 IX International Conference on Information Technology and Nanotechnology (ITNT).: IEEE, 2023. P. 1–5.

In this paper, we explore the possibility to improve efficiency of face recognition using information about anomaly input images. Indeed, modern publicly-available datasets typically contain images of mostly middle-aged and Caucasian people, which cause most algorithms to fail on photos of older people or children, rarer ethnicities, poor-quality images, etc. Detection of such anomaly data ...

Added: June 13, 2023

Сравнение различных методов реализации нейронных сетей для распознавания изображений

Головин Р. Д., Zunin V., В кн.: Межвузовская научно-техническая конференция студентов, аспирантов и молодых специалистов имени Е.В. Арменского. Материалы конференции.: М.: МИЭМ НИУ ВШЭ, 2021. С. 120–122.

В работе проводится обзор различных способов реализации нейронной сети для распознавания изображения на примере набора данных MNIST. Дается анализ используемых библиотек и методов их применения в области распознавания объектов. ...

Added: September 25, 2022

Sequential analysis in Fourier probabilistic neural networks

Savchenko A., Belova N. S., Expert Systems with Applications 2022 Vol. 207 Article 117885

In this paper, the computational complexity of the probabilistic neural network for the classification of high-dimensional data is improved. At first, the class probability densities are estimated by using only a few principal components of an observed point. The Gaussian–Parzen kernel is replaced by the orthogonal series estimates of class-conditional densities for each principal component using the Fourier series to speed ...

Added: June 29, 2022

Instagram Hashtag Prediction Using Deep Neural Networks

Anna Beketova, Makarov I., , in: Advances in Computational Intelligence: 16th International Work-Conference on Artificial Neural Networks, IWANN 2021, Virtual Event, June 16–18, 2021, Proceedings, Part II.: Cham: Springer, 2021. Ch. 3 P. 28–42.

*Реализация соц. сети Instagram запрещена на территории России по основаниям осуществления экстремистской деятельности. Instagram is one of the most popular photos sharing services. For more convenient content search people use hashtags (#nature, #love, etc.) in posts with photos. The author’s aim is to make hashtag prediction possible and convenient for users. The paper provides a reader with ...

Added: September 1, 2021

Fast inference in convolutional neural networks based on sequential three-way decisions

Savchenko A., Information Sciences 2021 Vol. 560 P. 370–385

A novel image recognition algorithm based on sequential three-way decisions is introduced to speed up the inference in a convolutional neural network. In contrast to the majority of existing studies, our approach does not require a special procedure to train a neural network, and thus it can be used with arbitrary architectures including pre-trained convolutional ...

Added: February 25, 2021

Вычислительно эффективные алгоритмы классификации изображений на основе последовательного анализа

Savchenko A., Записки научных семинаров ПОМИ РАН 2021 Т. 499 С. 267–283

In this paper fast image recognition techniques based on statistical sequential analysis are discussed. We examine the possibility to sequentially process the principal components and organize a convolutional neural net- work with early exits. Particular attention is paid to sequentially learn multi-task lightweight neural network model to predict several facial at- tributes (age, gender and ...

Added: January 27, 2021

Sequential Analysis with Specified Confidence Level and Adaptive Convolutional Neural Networks in Image Recognition

Savchenko A., , in: Proceedings of International Joint Conference on Neural Networks 2020 (IJCNN 2020).: Piscataway: IEEE, 2020. P. 1–8.

In this paper the problem of high computational complexity of deep convolutional nets in image recognition is considered. An existing framework of adaptive neural networks is extended by appending the separate classifier to intermediate layers. The hierarchical representations of the input image are sequentially analyzed. If the first classifier returns rather high confidence score, the ...

Added: October 15, 2020

Probabilistic Neural Network With Complex Exponential Activation Functions in Image Recognition

Savchenko A., IEEE Transactions on Neural Networks and Learning Systems 2020 Vol. 31 No. 2 P. 651–660

If the training data set in image recognition task is not very large, the feature extraction with a convolutional neural network is usually applied. Here, we focus on the nonparametric classification of extracted feature vectors using the probabilistic neural network (PNN). The latter is characterized by the high runtime and memory space complexity. We propose ...

Added: November 1, 2019

Computation-Efficient Face Recognition Algorithm Using a Sequential Analysis of High Dimensional Neural-Net Features

Sokolova A., Savchenko A., Optical Memory and Neural Networks (Information Optics) 2020 Vol. 29 No. 1 P. 19–29

The goal of the study is to increase the computation efficiency of the face recognition that uses feature vectors to describe facial images on photos and videos. These high-dimensional feature vectors are nowadays produced by convolutional neural networks. The methods to aggregate the features generated for each video frame are used to process the video ...

Added: October 25, 2019