A neural network based CBIR system using STI features and relevance feedback

Multimedia mining primarily involves, information analysis and retrieval based on implicit knowledge. The ever increasing digital image databases on the Internet has created a need for using multimedia mining on these databases for effective and efficient retrieval of images. Contents of an image can be expressed in different features such as Shape, Texture and Intensity-distribution(STI). Content Based Image Retrieval(CBIR) is an efficient retrieval of relevant images from large databases based on features extracted from the image. Most of the existing systems either concentrate on a single representation of all features or linear combination of these features. The paper proposes a CBIR System named STIRF (Shape, Texture, Intensity-distribution with Relevance Feedback) that uses a neural network for nonlinear combination of the heterogenous STI features. Further the system is self-adaptable to different applications and users based upon relevance feedback. Prior to retrieval of relevant images, each feature is first clustered independent of the other in its own space and this helps in matching of similar images. Testing the system on a database of images with varied contents and intensive backgrounds showed good results with most relevant images being retrieved for a image query. The system showed better and more robust performance compared to existing CBIR systems

CBIR Using Local and Global Properties of Image Sub-blocks

This paper proposes a content based image retrieval (CBIR) system using the local colour and texture features of selected image sub-blocks and global colour and shape features of the image. The image sub-blocks are roughly identified by segmenting the image into partitions of different configuration, finding the edge density in each partition using edge thresholding, morphological dilation and finding the corner density in each partition. The colour and texture features of the identified regions are computed from the histograms of the quantized HSV colour space and Gray Level Co- occurrence Matrix (GLCM) respectively. A combined colour and texture feature vector is computed for each region. The shape features are computed from the Edge Histogram Descriptor (EHD). Euclidean distance measure is used for computing the distance between the features of the query and target image. Experimental results show that the proposed method provides better retrieving result than retrieval using some of the existing methods

Class-specific metrics for multidimensional data projection applied to CBIR

Content-based image retrieval is still a challenging issue due to the inherent complexity of images and choice of the most discriminant descriptors. Recent developments in the field have introduced multidimensional projections to burst accuracy in the retrieval process, but many issues such as introduction of pattern recognition tasks and deeper user intervention to assist the process of choosing the most discriminant features still remain unaddressed. In this paper, we present a novel framework to CBIR that combines pattern recognition tasks, class-specific metrics, and multidimensional projection to devise an effective and interactive image retrieval system. User interaction plays an essential role in the computation of the final multidimensional projection from which image retrieval will be attained. Results have shown that the proposed approach outperforms existing methods, turning out to be a very attractive alternative for managing image data sets.

PRoSPer: perceptual similarity queries in medical CBIR systems through user profiles

In this paper, we present a novel approach to perform similarity queries over medical images, maintaining the semantics of a given query posted by the user. Content-based image retrieval systems relying on relevance feedback techniques usually request the users to label relevant/irrelevant images. Thus, we present a highly effective strategy to survey user profiles, taking advantage of such labeling to implicitly gather the user perceptual similarity. The profiles maintain the settings desired for each user, allowing tuning of the similarity assessment, which encompasses the dynamic change of the distance function employed through an interactive process. Experiments on medical images show that the method is effective and can improve the decision making process during analysis.

Descrittori e metriche di similarita per applicazioni cbir.

Questo elaborato analizza alcuni dei principali metodi di estrazione del contenuto di un'immagine digitale, descrivendone il funzionamento. É possibile consultare un'ampia quantità di test effettuati su 7 dataset con caratteristiche eterogenee al fine di valutarne le performance a seconda dei casi di utilizzo.

O uso da Divergência de Kullback-Leibler e da Divergência Generalizada como medida de similaridade em sistemas CBIR

The content-based image retrieval is important for various purposes like disease diagnoses from computerized tomography, for example. The relevance, social and economic of image retrieval systems has created the necessity of its improvement. Within this context, the content-based image retrieval systems are composed of two stages, the feature extraction and similarity measurement. The stage of similarity is still a challenge due to the wide variety of similarity measurement functions, which can be combined with the different techniques present in the recovery process and return results that aren’t always the most satisfactory. The most common functions used to measure the similarity are the Euclidean and Cosine, but some researchers have noted some limitations in these functions conventional proximity, in the step of search by similarity. For that reason, the Bregman divergences (Kullback Leibler and I-Generalized) have attracted the attention of researchers, due to its flexibility in the similarity analysis. Thus, the aim of this research was to conduct a comparative study over the use of Bregman divergences in relation the Euclidean and Cosine functions, in the step similarity of content-based image retrieval, checking the advantages and disadvantages of each function. For this, it was created a content-based image retrieval system in two stages: offline and online, using approaches BSM, FISM, BoVW and BoVW-SPM. With this system was created three groups of experiments using databases: Caltech101, Oxford and UK-bench. The performance of content-based image retrieval system using the different functions of similarity was tested through of evaluation measures: Mean Average Precision, normalized Discounted Cumulative Gain, precision at k, precision x recall. Finally, this study shows that the use of Bregman divergences (Kullback Leibler and Generalized) obtains better results than the Euclidean and Cosine measures with significant gains for content-based image retrieval.

Efficient content-based image retrieval based on multi-feature fusion

The increased availability of image capturing devices has enabled collections of digital images to rapidly expand in both size and diversity. This has created a constantly growing need for efficient and effective image browsing, searching, and retrieval tools. Pseudo-relevance feedback (PRF) has proven to be an effective mechanism for improving retrieval accuracy. An original, simple yet effective rank-based PRF mechanism (RB-PRF) that takes into account the initial rank order of each image to improve retrieval accuracy is proposed. This RB-PRF mechanism innovates by making use of binary image signatures to improve retrieval precision by promoting images similar to highly ranked images and demoting images similar to lower ranked images. Empirical evaluations based on standard benchmarks, namely Wang, Oliva & Torralba, and Corel datasets demonstrate the effectiveness of the proposed RB-PRF mechanism in image retrieval.

Plasticité intermodale chez le hamster énucléé à la naissance : Études de la distribution des interneurones CaBPir dans les cortex visuel et auditif primaires.

La période postnatale et l’expérience sensorielle sont critiques pour le développement du système visuel. Les interneurones inhibiteurs exprimant l’acide γ-aminobutyrique (GABA) jouent un rôle important dans le contrôle de l’activité neuronale, le raffinement et le traitement de l’information sensorielle qui parvient au cortex cérébral. Durant le développement, lorsque le cortex cérébral est très susceptible aux influences extrinsèques, le GABA agit dans la formation des périodes critiques de sensibilité ainsi que dans la plasticité dépendante de l’expérience. Ainsi, ce système inhibiteur servirait à ajuster le fonctionnement des aires sensorielles primaires selon les conditions spécifiques d’activité en provenance du milieu, des afférences corticales (thalamiques et autres) et de l’expérience sensorielle. Certaines études montrent que des différences dans la densité et la distribution de ces neurones inhibiteurs corticaux reflètent les caractéristiques fonctionnelles distinctes entre les différentes aires corticales. La Parvalbumine (PV), la Calretinine (CR) et la Calbindine (CB) sont des protéines chélatrices du calcium (calcium binding proteins ou CaBPs) localisées dans différentes sous-populations d’interneurones GABAergiques corticaux. Ces protéines tamponnent le calcium intracellulaire de sorte qu’elles peuvent moduler différemment plusieurs fonctions neuronales, notamment l’aspect temporel des potentiels d’action, la transmission synaptique et la potentialisation à long terme. Plusieurs études récentes montrent que les interneurones immunoréactifs (ir) aux CaBPs sont également très sensibles à l’expérience et à l’activité sensorielle durant le développement et chez l’adulte. Ainsi, ces neurones pourraient avoir un rôle crucial à jouer dans le phénomène de compensation ou de plasticité intermodale entre les cortex sensoriels primaires. Chez le hamster (Mesocricetus auratus), l’énucléation à la naissance fait en sorte que le cortex visuel primaire peut être recruté par les autres modalités sensorielles, telles que le toucher et l’audition. Suite à cette privation oculaire, il y a établissement de projections ectopiques permanentes entre les collicules inférieurs (CI) et le corps genouillé latéral (CGL). Ceci a pour effet d’acheminer l’information auditive vers le cortex visuel primaire (V1) durant le développement postnatal. À l’aide de ce modèle, l’objectif général de ce projet de thèse est d’étudier l’influence et le rôle de l’activité sensorielle sur la distribution et l’organisation des interneurones corticaux immunoréactifs aux CaBPs dans les aires sensorielles visuelle et auditive primaires du hamster adulte. Les changements dans l’expression des CaBPs ont été déterminés d’une manière quantitative en évaluant les profils de distribution laminaire de ces neurones révélés par immunohistochimie. Dans une première expérience, nous avons étudié la distribution laminaire des CaBPs dans les aires visuelle (V1) et auditive (A1) primaires chez le hamster normal adulte. Les neurones immunoréactifs à la PV et la CB, mais non à la CR, sont distribués différemment dans ces deux cortex primaires dédiés à une modalité sensorielle différente. Dans une deuxième étude, une comparaison a été effectuée entre des animaux contrôles et des hamsters énucléés à la naissance. Cette étude montre que le cortex visuel primaire de ces animaux adopte une chimioarchitecture en PV similaire à celle du cortex auditif. Nos recherches montrent donc qu’une suppression de l’activité visuelle à la naissance peut influencer l’expression des CaBPs dans l’aire V1 du hamster adulte. Ceci suggère également que le type d’activité des afférences en provenance d’autres modalités sensorielles peut moduler, en partie, une circuiterie corticale en CaBPs qui lui est propre dans le cortex hôte ou recruté. Ainsi, nos travaux appuient l’hypothèse selon laquelle il serait possible que certaines de ces sous-populations d’interneurones GABAergiques jouent un rôle crucial dans le phénomène de la plasticité intermodale.

Content Based Image Retrieval Using Low Level Features of Automatically Extracted Regions of Interest

This paper proposes a region based image retrieval system using the local colour and texture features of image sub regions. The regions of interest (ROI) are roughly identified by segmenting the image into fixed partitions, finding the edge map and applying morphological dilation. The colour and texture features of the ROIs are computed from the histograms of the quantized HSV colour space and Gray Level co- occurrence matrix (GLCM) respectively. Each ROI of the query image is compared with same number of ROIs of the target image that are arranged in the descending order of white pixel density in the regions, using Euclidean distance measure for similarity computation. Preliminary experimental results show that the proposed method provides better retrieving result than retrieval using some of the existing methods.

A Sub-block Based Image Retrieval Using Modified Integrated Region Matching.

This paper proposes a content based image retrieval (CBIR) system using the local colour and texture features of selected image sub-blocks and global colour and shape features of the image. The image sub-blocks are roughly identified by segmenting the image into partitions of different configuration, finding the edge density in each partition using edge thresholding, morphological dilation. The colour and texture features of the identified regions are computed from the histograms of the quantized HSV colour space and Gray Level Co- occurrence Matrix (GLCM) respectively. A combined colour and texture feature vector is computed for each region. The shape features are computed from the Edge Histogram Descriptor (EHD). A modified Integrated Region Matching (IRM) algorithm is used for finding the minimum distance between the sub-blocks of the query and target image. Experimental results show that the proposed method provides better retrieving result than retrieval using some of the existing methods

Grey Level Co-Occurrence Matrices: Generalisation And Some New Features

Grey Level Co-occurrence Matrices (GLCM) are one of the earliest techniques used for image texture analysis. In this paper we defined a new feature called trace extracted from the GLCM and its implications in texture analysis are discussed in the context of Content Based Image Retrieval (CBIR). The theoretical extension of GLCM to n-dimensional gray scale images are also discussed. The results indicate that trace features outperform Haralick features when applied to CBIR.

Content Based Image Retrieval System for Malayalam Handwritten Characters

Content Based Image Retrieval is one of the prominent areas in Computer Vision and Image Processing. Recognition of handwritten characters has been a popular area of research for many years and still remains an open problem. The proposed system uses visual image queries for retrieving similar images from database of Malayalam handwritten characters. Local Binary Pattern (LBP) descriptors of the query images are extracted and those features are compared with the features of the images in database for retrieving desired characters. This system with local binary pattern gives excellent retrieval performance

Automatic Image Annotation Using SURF Descriptors

In recent years there is an apparent shift in research from content based image retrieval (CBIR) to automatic image annotation in order to bridge the gap between low level features and high level semantics of images. Automatic Image Annotation (AIA) techniques facilitate extraction of high level semantic concepts from images by machine learning techniques. Many AIA techniques use feature analysis as the first step to identify the objects in the image. However, the high dimensional image features make the performance of the system worse. This paper describes and evaluates an automatic image annotation framework which uses SURF descriptors to select right number of features and right features for annotation. The proposed framework uses a hybrid approach in which k-means clustering is used in the training phase and fuzzy K-NN classification in the annotation phase. The performance of the system is evaluated using standard metrics.

Performance Study of an Improved Legendre Moment Descriptor as Region-Based Shape Descriptor

This paper reports a novel region-based shape descriptor based on orthogonal Legendre moments. The preprocessing steps for invariance improvement of the proposed Improved Legendre Moment Descriptor (ILMD) are discussed. The performance of the ILMD is compared to the MPEG-7 approved region shape descriptor, angular radial transformation descriptor (ARTD), and the widely used Zernike moment descriptor (ZMD). Set B of the MPEG-7 CE-1 contour database and all the datasets of the MPEG-7 CE-2 region database were used for experimental validation. The average normalized modified retrieval rate (ANMRR) and precision- recall pair were employed for benchmarking the performance of the candidate descriptors. The ILMD has lower ANMRR values than ARTD for most of the datasets, and ARTD has a lower value compared to ZMD. This indicates that overall performance of the ILMD is better than that of ARTD and ZMD. This result is confirmed by the precision-recall test where ILMD was found to have better precision rates for most of the datasets tested. Besides retrieval accuracy, ILMD is more compact than ARTD and ZMD. The descriptor proposed is useful as a generic shape descriptor for content-based image retrieval (CBIR) applications