Image Coding with Face Descriptors Embedding

4siContent descriptors, useful for browsing and retrieval tasks, are generally extracted and treated as a separate entity with respect to the nature of the content itself. At the same time, conventional coding processes do not take into account information carried out by content descriptors. Content descriptors are closely related to the content itself, and they potentially can be used to exploit redundancy in entropy coding processes. Embedding content descriptors in the bitstream can reduce content description extraction load, and at the same time, it can reduce the rate associated to the compressed content and its description. In this paper an effective implementation of this approach is presented, where image descriptors are actively used in the coding process for exploiting redundancy. First of all, image areas containing faces are detected and encoded using a scalable method, where the base layer is represented by the corresponding eigenface, and the enhancement layer is formed by the prediction error. The remaining areas are then encoded by using a traditional approach. Simulations show that achievable compression performances are comparable with those provided by conventional, making the proposed approach very convenient for source coding and content description.partially_openpartially_openBoschetti A.; Adami N.; Leonardi R.; Okuda M.Boschetti, Alberto; Adami, Nicola; Leonardi, Riccardo; Okuda, M

Subband domain coding of binary textual images for document archiving

Cataloged from PDF version of article.In this work, a subband domain textual image compression method is developed. The document image is first decomposed into subimages using binary subband decompositions. Next, the character locations in the subbands and the symbol library consisting of the character images are encoded. The method is suitable for keyword search in the compressed data. It is observed that very high compression ratios are obtained with this method. Simulation studies are presented

High Dynamic Range Images Coding: Embedded and Multiple Description

The aim of this work is to highlight and discuss a new paradigm for representing high-dynamic range (HDR) images that can be used for both its coding and describing its multimedia content. In particular, the new approach defines a new representation domain that, conversely from the classical compressed one, enables to identify and exploit content metadata. Information related to content are used here to control both the encoding and the decoding process and are directly embedded in the compressed data stream. Firstly, thanks to the proposed solution, the content description can be quickly accessed without the need of fully decoding the compressed stream. This fact ensures a significant improvement in the performance of search and retrieval systems, such as for semantic browsing of image databases. Then, other potential benefits can be envisaged especially in the field of management and distribution of multimedia content, because the direct embedding of content metadata preserves the consistency between content stream and content description without the need of other external frameworks, such as MPEG-21. The paradigm proposed here may also be shifted to Multiple description coding, where different representations of the HDR image can be generated accordingly to its content. The advantages provided by the new proposed method are visible at different levels, i.e. when evaluating the redundancy reduction. Moreover, the descriptors extracted from the compressed data stream could be actively used in complex applications, such as fast retrieval of similar images from huge databases

Iterative EEG-based Natural Image Search under RSVP

This work extends previous studies on using EEG decoding for automatic image retrieval. We propose an iterative way to integrate the information obtained from the EEG decoding and image processing methods. In the light of real-world BCI applications, we demonstrated that a limited number of EEG channels provide sufficient information about the subject’s preference to be exploited in image retrieval by the proposed synergistic scenario. Furthermore, to meet a more realistic scenario we used natural images (i.e., images of objects in their natural environment)

Subband domain coding of binary textual images for document archiving

In this work, a subband domain textual image compression method is developed. The document image is first decomposed into subimages using binary subband decompositions. Next, the character locations in the subbands and the symbol library consisting of the character images are encoded. The method is suitable for keyword search in the compressed data. It is observed that very high compression ratios are obtained with this method. Simulation studies are presented

An Iterative Framework for EEG-based Image Search: Robust Retrieval with Weak Classifiers

We revisit the framework for brain-coupled image search, where the Electroencephalography (EEG) channel under rapid serial visual presentation protocol is used to detect user preferences. Extending previous works on the synergy between content-based image labeling and EEG-based brain-computer interface (BCI), we propose a different perspective on iterative coupling. Previously, the iterations were used to improve the set of EEG-based image labels before propagating them to the unseen images for the final retrieval. In our approach we accumulate the evidence of the true labels for each image in the database through iterations. This is done by propagating the EEG-based labels of the presented images at each iteration to the rest of images in the database. Our results demonstrate a continuous improvement of the labeling performance across iterations despite the moderate EEG-based labeling (AUC <75%). The overall analysis is done in terms of the single-trial EEG decoding performance and the image database reorganization quality. Furthermore, we discuss the EEG-based labeling performance with respect to a search task given the same image database

Characterisation and adaptive learning in interactive video retrieval

El objetivo principal de esta tesis consiste en utilizar eficazmente los modelos de tópicos latentes para afrontar el problema de la recuperación automática de vídeo. Concretamente, se pretende mejorar tanto a nivel de eficiencia como a nivel de precisión el actual estado del arte en materia de los sitemas de recuperación automática de vídeo. En general, los modelos de tópicos latentes son un conjunto de herramientas estadísticas que permiten extraer los patrones generadores de una colección de datos. Tradicionalmente, este tipo de técnicas no han sido consideradas de gran utilidad para los sistemas de recuperación automática de vídeo debido a su alto coste computacional y a la propia complejidad del espacio de tópicos en el ámbito de la información visual.In this work, we are interested in the use of latent topics to overcome the current limitations in CBVR. Despite the potential of topic models to uncover the hidden structure of a collection, they have traditionally been unable to provide a competitive advantage in CBVR because of the high computational cost of their algorithms and the complexity of the latent space in the visual domain. Throughout this thesis we focus on designing new models and tools based on topic models to take advantage of the latent space in CBVR. Specifically, we have worked in four different areas within the retrieval process: vocabulary reduction, encoding, modelling and ranking, being our most important contributions related to both modelling and ranking

Discriminative learning with application to interactive facial image retrieval

The amount of digital images is growing drastically and advanced tools for searching in large image collections are therefore becoming urgently needed. Content-based image retrieval is advantageous for such a task in terms of automatic feature extraction and indexing without human labor and subjectivity in image annotations. The semantic gap between high-level semantics and low-level visual features can be reduced by the relevance feedback technique. However, most existing interactive content-based image retrieval (ICBIR) systems require a substantial amount of human evaluation labor, which leads to the evaluation fatigue problem that heavily restricts the application of ICBIR. In this thesis a solution based on discriminative learning is presented. It extends an existing ICBIR system, PicSOM, towards practical applications. The enhanced ICBIR system allows users to input partial relevance which includes not only relevance extent but also relevance reason. A multi-phase retrieval with partial relevance can adapt to the user's searching intention in a from-coarse-to-fine manner. The retrieval performance can be improved by employing supervised learning as a preprocessing step before unsupervised content-based indexing. In this work, Parzen Discriminant Analysis (PDA) is proposed to extract discriminative components from images. PDA regularizes the Informative Discriminant Analysis (IDA) objective with a greatly accelerated optimization algorithm. Moreover, discriminative Self-Organizing Maps trained with resulting features can easily handle fuzzy categorizations. The proposed techniques have been applied to interactive facial image retrieval. Both a query example and a benchmark simulation study are presented, which indicate that the first image depicting the target subject can be retrieved in a small number of rounds

AUTOMATED FEATURE EXTRACTION AND CONTENT-BASED RETRIEVAL OFPATHOLOGY MICROSCOPIC IMAGES USING K-MEANS CLUSTERING AND CODE RUN-LENGTH PROBABILITY DISTRIBUTION

The dissertation starts with an extensive literature survey on the current issues in content-based image retrieval (CBIR) research, the state-of-the-art theories, methodologies, and implementations, covering topics such as general information retrieval theories, imaging, image feature identification and extraction, feature indexing and multimedia database search, user-system interaction, relevance feedback, and performance evaluation. A general CBIR framework has been proposed with three layers: image document space, feature space, and concept space. The framework emphasizes that while the projection from the image document space to the feature space is algorithmic and unrestricted, the connection between the feature space and the concept space is based on statistics instead of semantics. The scheme favors image features that do not rely on excessive assumptions about image contentAs an attempt to design a new CBIR methodology following the above framework, k-means clustering color quantization is applied to pathology microscopic images, followed by code run-length probability distribution feature extraction. Kulback-Liebler divergence is used as distance measure for feature comparison. For content-based retrieval, the distance between two images is defined as a function of all individual features. The process is highly automated and the system is capable of working effectively across different tissues without human interference. Possible improvements and future directions have been discussed