Voronoi-Based Compact Image Descriptors: Efficient Region-of-Interest Retrieval With VLAD and Deep-Learning-Based Descriptors

We investigate the problem of image retrieval based on visual queries when the latter comprise arbitrary regionsof- interest (ROI) rather than entire images. Our proposal is a compact image descriptor that combines the state-of-the-art in content-based descriptor extraction with a multi-level, Voronoibased spatial partitioning of each dataset image. The proposed multi-level Voronoi-based encoding uses a spatial hierarchical K-means over interest-point locations, and computes a contentbased descriptor over each cell. In order to reduce the matching complexity with minimal or no sacrifice in retrieval performance: (i) we utilize the tree structure of the spatial hierarchical Kmeans to perform a top-to-bottom pruning for local similarity maxima; (ii) we propose a new image similarity score that combines relevant information from all partition levels into a single measure for similarity; (iii) we combine our proposal with a novel and efficient approach for optimal bit allocation within quantized descriptor representations. By deriving both a Voronoi-based VLAD descriptor (termed as Fast-VVLAD) and a Voronoi-based deep convolutional neural network (CNN) descriptor (termed as Fast-VDCNN), we demonstrate that our Voronoi-based framework is agnostic to the descriptor basis, and can easily be slotted into existing frameworks. Via a range of ROI queries in two standard datasets, it is shown that the Voronoibased descriptors achieve comparable or higher mean Average Precision against conventional grid-based spatial search, while offering more than two-fold reduction in complexity. Finally, beyond ROI queries, we show that Voronoi partitioning improves the geometric invariance of compact CNN descriptors, thereby resulting in competitive performance to the current state-of-theart on whole image retrieval

Investigation on advanced image search techniques

Content-based image search for retrieval of images based on the similarity in their visual contents, such as color, texture, and shape, to a query image is an active research area due to its broad applications. Color, for example, provides powerful information for image search and classification. This dissertation investigates advanced image search techniques and presents new color descriptors for image search and classification and robust image enhancement and segmentation methods for iris recognition. First, several new color descriptors have been developed for color image search. Specifically, a new oRGB-SIFT descriptor, which integrates the oRGB color space and the Scale-Invariant Feature Transform (SIFT), is proposed for image search and classification. The oRGB-SIFT descriptor is further integrated with other color SIFT features to produce the novel Color SIFT Fusion (CSF), the Color Grayscale SIFT Fusion (CGSF), and the CGSF+PHOG descriptors for image category search with applications to biometrics. Image classification is implemented using a novel EFM-KNN classifier, which combines the Enhanced Fisher Model (EFM) and the K Nearest Neighbor (KNN) decision rule. Experimental results on four large scale, grand challenge datasets have shown that the proposed oRGB-SIFT descriptor improves recognition performance upon other color SIFT descriptors, and the CSF, the CGSF, and the CGSF+PHOG descriptors perform better than the other color SIFT descriptors. The fusion of both Color SIFT descriptors (CSF) and Color Grayscale SIFT descriptor (CGSF) shows significant improvement in the classification performance, which indicates that various color-SIFT descriptors and grayscale-SIFT descriptor are not redundant for image search. Second, four novel color Local Binary Pattern (LBP) descriptors are presented for scene image and image texture classification. Specifically, the oRGB-LBP descriptor is derived in the oRGB color space. The other three color LBP descriptors, namely, the Color LBP Fusion (CLF), the Color Grayscale LBP Fusion (CGLF), and the CGLF+PHOG descriptors, are obtained by integrating the oRGB-LBP descriptor with some additional image features. Experimental results on three large scale, grand challenge datasets have shown that the proposed descriptors can improve scene image and image texture classification performance. Finally, a new iris recognition method based on a robust iris segmentation approach is presented for improving iris recognition performance. The proposed robust iris segmentation approach applies power-law transformations for more accurate detection of the pupil region, which significantly reduces the candidate limbic boundary search space for increasing detection accuracy and efficiency. As the limbic circle, which has a center within a close range of the pupil center, is selectively detected, the eyelid detection approach leads to improved iris recognition performance. Experiments using the Iris Challenge Evaluation (ICE) database show the effectiveness of the proposed method

Contributions on Automatic Recognition of Faces using Local Texture Features

Uno de los temas más destacados del área de visión artifical se deriva del análisis facial automático. En particular, la detección precisa de caras humanas y el análisis biométrico de las mismas son problemas que han generado especial interés debido a la gran cantidad de aplicaciones que actualmente hacen uso de estos mecnismos. En esta Tesis Doctoral se analizan por separado los problemas relacionados con detección precisa de caras basada en la localización de los ojos y el reconomcimiento facial a partir de la extracción de características locales de textura. Los algoritmos desarrollados abordan el problema de la extracción de la identidad a partir de una imagen de cara ( en vista frontal o semi-frontal), para escenarios parcialmente controlados. El objetivo es desarrollar algoritmos robustos y que puedan incorpararse fácilmente a aplicaciones reales, tales como seguridad avanzada en banca o la definición de estrategias comerciales aplicadas al sector de retail. Respecto a la extracción de texturas locales, se ha realizado un análisis exhaustivo de los descriptores más extendidos; se ha puesto especial énfasis en el estudio de los Histogramas de Grandientes Orientados (HOG features). En representaciones normalizadas de la cara, estos descriptores ofrecen información discriminativa de los elementos faciales (ojos, boca, etc.), siendo robustas a variaciones en la iluminación y pequeños desplazamientos. Se han elegido diferentes algoritmos de clasificación para realizar la detección y el reconocimiento de caras, todos basados en una estrategia de sistemas supervisados. En particular, para la localización de ojos se ha utilizado clasificadores boosting y Máquinas de Soporte Vectorial (SVM) sobre descriptores HOG. En el caso de reconocimiento de caras, se ha desarrollado un nuevo algoritmo, HOG-EBGM (HOG sobre Elastic Bunch Graph Matching). Dada la imagen de una cara, el esquema seguido por este algoritmo se puede resumir en pocos pasos: en una primera etapa se extMonzó Ferrer, D. (2012). Contributions on Automatic Recognition of Faces using Local Texture Features [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/16698Palanci