Recod @ Mediaeval 2014: Diverse Social Images Retrieval

This paper presents the results of the rst participation of our multi-institutional team in the Retrieving Diverse Social Images Task at MediaEval 2014. In this task we were required to develop a summarization and diversi cation approach for social photo retrieval. Our approach is based on irrelevant image ltering, image re-ranking, and diversity promotion by clustering. We have used visual and textual features, including image metadata and user credibility information.1263Carbonell, J., Goldstein, J., The use of mmr, diversity-based reranking for reordering documents and producing summaries (1998) SIGIR, pp. 335-336Ionescu, B., Popescu, A., Lupu, M., Gînscâ, A.L., MüLler, H., Retrieving diverse social images at mediaeval 2014: Challenge, dataset and evaluation (2014) MediaEval 2014 Workshop, , BarcelonaPenatti, O.A.B., Valle, E., Da Torres, R.S., Comparative study of global color and texture descriptors for web image retrieval (2012) J. Vis. Commun. Image Repr., 23 (2), pp. 359-38

COLOR FEATURE WITH SPATIAL INFORMATION EXTRACTION METHODS FOR CBIR: A REVIEW

Inn then last two decades the Content Based Image Retrieval (CBIR) considered as one of the topic of interest for theresearchers. It depending one analysis of the image’s visual content which can be done by extracting the color, texture and shapefeatures. Therefore, feature extraction is one of the important steps in CBIR system for representing the image completely. Color featureis the most widely used and more reliable feature among the image visual features. This paper reviews different methods, namely LocalColor Histogram, Color Correlogram, Row sum and Column sum and Colors Coherences Vectors were used to extract colors featurestaking in consideration the spatial information of the image

FUSÃO DE RANKINGS PARA RECUPERAÇÃO DE IMAGENS

Dada a complexidade da consulta ou a pouca informação disponível para a configuraçãode um sistema de busca de imagens com realimentação de relevância, o conjunto inicialde resultados, anterior à primeira interação do usuário, pode não apresentar informaçõesrelevantes suficientes. Isso pode acarretar em um feedback com pouca informação econsequente dificultar a etapa de aprendizado. Por ser uma abordagem interativa, ainfluência do primeiro resultado propaga-se para as demais iterações, dado que oresultado inicial ruim limita a troca de informação entre o usuário e o sistema (Calumbyet al , 2017) e consequentemente os modelos de aprendizado da intenção do usuário.Visando atenuar este problema, é imprescindível que o resultado da primeira interaçãoseja o melhor possível

Fuzzy Color Space for Apparel Coordination

Human perception of colors constitutes an important part in color theory. The applications of color science are truly omnipresent, and what impression colors make on human plays a vital role in them. In this paper, we offer the novel approach for color information representation and processing using fuzzy sets and logic theory, which is extremely useful in modeling human impressions. Specifically, we use fuzzy mathematics to partition the gamut of feasible colors in HSI color space based on standard linguistic tags. The proposed method can be useful in various image processing applications involving query processing. We demonstrate its effectivity in the implementation of a framework for the apparel online shopping coordination based on a color scheme. It deserves attention, since there is always some uncertainty inherent in the description of apparels

Swarm-based Descriptor Combination and its Application for Image Classification

In this paper, we deal with the descriptor combination problem in image classification tasks. This problem refers to the definition of an appropriate combination of image content descriptors that characterize different visual properties, such as color, shape and texture. In this paper, we propose to model the descriptor combination as a swarm-based optimization problem, which finds out the set of parameters that maximizes the classification accuracy of the Optimum-Path Forest (OPF) classifier. In our model, a descriptor is seen as a pair composed of a feature extraction algorithm and a suitable distance function. Our strategy here is to combine distance scores defined by different descriptors, as well as to employ them to weight OPF edges, which connect samples in the feature space. An extensive evaluation of several swarm-based optimization techniques was performed. Experimental results have demonstrated the robustness of the proposed combination approach

Swarm-based Descriptor Combination and its Application for Image Classification

In this paper, we deal with the descriptor combination problem in image classification tasks. This problem refers to the definition of an appropriate combination of image content descriptors that characterize different visual properties, such as color, shape and texture. In this paper, we propose to model the descriptor combination as a swarm-based optimization problem, which finds out the set of parameters that maximizes the classification accuracy of the Optimum-Path Forest (OPF) classifier. In our model,  a descriptor is seen as a pair composed of a feature extraction algorithm and a suitable distance function. Our strategy here is to combine distance scores defined by different descriptors, as well as to employ them to weight OPF edges, which connect samples in the feature space. An extensive evaluation of several swarm-based optimization techniques was performed. Experimental results have demonstrated the robustness of the proposed combination approach

Rotation Invariant on Harris Interest Points for Exposing Image Region Duplication Forgery

Nowadays, image forgery has become common because only an editing package software and a digital camera are required to counterfeit an image. Various fraud detection systems have been developed in accordance with the requirements of numerous applications and to address different types of image forgery. However, image fraud detection is a complicated process given that is necessary to identify the image processing tools used to counterfeit an image. Here, we describe recent developments in image fraud detection. Conventional techniques for detecting duplication forgeries have difficulty in detecting postprocessing falsification, such as grading and joint photographic expert group compression. This study proposes an algorithm that detects image falsification on the basis of Hessian features