Investigation of new feature descriptors for image search and classification

Content-based image search, classification and retrieval is an active and important research area due to its broad applications as well as the complexity of the problem. Understanding the semantics and contents of images for recognition remains one of the most difficult and prevailing problems in the machine intelligence and computer vision community. With large variations in size, pose, illumination and occlusions, image classification is a very challenging task. A good classification framework should address the key issues of discriminatory feature extraction as well as efficient and accurate classification. Towards that end, this dissertation focuses on exploring new image descriptors by incorporating cues from the human visual system, and integrating local, texture, shape as well as color information to construct robust and effective feature representations for advancing content-based image search and classification. Based on the Gabor wavelet transformation, whose kernels are similar to the 2D receptive field profiles of the mammalian cortical simple cells, a series of new image descriptors is developed. Specifically, first, a new color Gabor-HOG (GHOG) descriptor is introduced by concatenating the Histograms of Oriented Gradients (HOG) of the component images produced by applying Gabor filters in multiple scales and orientations to encode shape information. Second, the GHOG descriptor is analyzed in six different color spaces and grayscale to propose different color GHOG descriptors, which are further combined to present a new Fused Color GHOG (FC-GHOG) descriptor. Third, a novel GaborPHOG (GPHOG) descriptor is proposed which improves upon the Pyramid Histograms of Oriented Gradients (PHOG) descriptor, and subsequently a new FC-GPHOG descriptor is constructed by combining the multiple color GPHOG descriptors and employing the Principal Component Analysis (PCA). Next, the Gabor-LBP (GLBP) is derived by accumulating the Local Binary Patterns (LBP) histograms of the local Gabor filtered images to encode texture and local information of an image. Furthermore, a novel Gabor-LBPPHOG (GLP) image descriptor is proposed which integrates the GLBP and the GPHOG descriptors as a feature set and an innovative Fused Color Gabor-LBP-PHOG (FC-GLP) is constructed by fusing the GLP from multiple color spaces. Subsequently, The GLBP and the GHOG descriptors are then combined to produce the Gabor-LBP-HOG (GLH) feature vector which performs well on different object and scene image categories. The six color GLH vectors are further concatenated to form the Fused Color GLH (FC-GLH) descriptor. Finally, the Wigner based Local Binary Patterns (WLBP) descriptor is proposed that combines multi-neighborhood LBP, Pseudo-Wigner distribution of images and the popular bag of words model to effectively classify scene images. To assess the feasibility of the proposed new image descriptors, two classification methods are used: one method applies the PCA and the Enhanced Fisher Model (EFM) for feature extraction and the nearest neighbor rule for classification, while the other method employs the Support Vector Machine (SVM). The classification performance of the proposed descriptors is tested on several publicly available popular image datasets. The experimental results show that the proposed new image descriptors achieve image search and classification results better than or at par with other popular image descriptors, such as the Scale Invariant Feature Transform (SIFT), the Pyramid Histograms of visual Words (PHOW), the Pyramid Histograms of Oriented Gradients (PHOG), the Spatial Envelope (SE), the Color SIFT four Concentric Circles (C4CC), the Object Bank (OB), the Context Aware Topic Model (CA-TM), the Hierarchical Matching Pursuit (HMP), the Kernel Spatial Pyramid Matching (KSPM), the SIFT Sparse-coded Spatial Pyramid Matching (Sc-SPM), the Kernel Codebook (KC) and the LBP

Identifying person re-occurrences for personal photo management applications

Automatic identification of "who" is present in individual digital images within a photo management system using only content-based analysis is an extremely difficult problem. The authors present a system which enables identification of person reoccurrences within a personal photo management application by combining image content-based analysis tools with context data from image capture. This combined system employs automatic face detection and body-patch matching techniques, which collectively facilitate identifying person re-occurrences within images grouped into events based on context data. The authors introduce a face detection approach combining a histogram-based skin detection model and a modified BDF face detection method to detect multiple frontal faces in colour images. Corresponding body patches are then automatically segmented relative to the size, location and orientation of the detected faces in the image. The authors investigate the suitability of using different colour descriptors, including MPEG-7 colour descriptors, color coherent vectors (CCV) and color correlograms for effective body-patch matching. The system has been successfully integrated into the MediAssist platform, a prototype Web-based system for personal photo management, and runs on over 13000 personal photos

Unsupervised Graph-based Rank Aggregation for Improved Retrieval

This paper presents a robust and comprehensive graph-based rank aggregation approach, used to combine results of isolated ranker models in retrieval tasks. The method follows an unsupervised scheme, which is independent of how the isolated ranks are formulated. Our approach is able to combine arbitrary models, defined in terms of different ranking criteria, such as those based on textual, image or hybrid content representations. We reformulate the ad-hoc retrieval problem as a document retrieval based on fusion graphs, which we propose as a new unified representation model capable of merging multiple ranks and expressing inter-relationships of retrieval results automatically. By doing so, we claim that the retrieval system can benefit from learning the manifold structure of datasets, thus leading to more effective results. Another contribution is that our graph-based aggregation formulation, unlike existing approaches, allows for encapsulating contextual information encoded from multiple ranks, which can be directly used for ranking, without further computations and post-processing steps over the graphs. Based on the graphs, a novel similarity retrieval score is formulated using an efficient computation of minimum common subgraphs. Finally, another benefit over existing approaches is the absence of hyperparameters. A comprehensive experimental evaluation was conducted considering diverse well-known public datasets, composed of textual, image, and multimodal documents. Performed experiments demonstrate that our method reaches top performance, yielding better effectiveness scores than state-of-the-art baseline methods and promoting large gains over the rankers being fused, thus demonstrating the successful capability of the proposal in representing queries based on a unified graph-based model of rank fusions