On Building a Universal and Compact Visual Vocabulary

Bag-of-visual-words has been shown to be a powerful image representation and attained great success in many computer vision and pattern recognition applications. Usually, for a given dataset, researchers choose to build a specific visual vocabulary from the dataset, and the problem of deriving a universal visual vocabulary is rarely addressed. Based on previous work on the classification performance with respect to visual vocabulary sizes, we arrive at a hypothesis that a universal visual vocabulary can be obtained by taking-into account the similarity extent of keypoints represented by one visual word. We then propose to use a similarity threshold-based clustering method to calculate the optimal vocabulary size, where the universal similarity threshold can be obtained empirically. With the optimal vocabulary size, the optimal visual vocabularies of limited sizes from three datasets are shown to be exchangeable and therefore universal. This result indicates that a universal and compact visual vocabulary can be built from a not too small dataset. Our work narrows the gab between bag-of-visual-words and bag-of-words, where a relatively fixed vocabulary can be used with different text datasets

Aggregating Local Features into Bundles for High-Precision Object Retrieval

Due to the omnipresence of digital cameras and mobile phones the number of images stored in image databases has grown tremendously in the last years. It becomes apparent that new data management and retrieval techniques are needed to deal with increasingly large image databases. This thesis presents new techniques for content-based image retrieval where the image content itself is used to retrieve images by visual similarity from databases. We focus on the query-by-example scenario, assuming the image itself is provided as query to the retrieval engine. In many image databases, images are often associated with metadata, which may be exploited to improve the retrieval performance. In this work, we present a technique that fuses cues from the visual domain and textual annotations into a single compact representation. This combined multimodal representation performs significantly better compared to the underlying unimodal representations, which we demonstrate on two large-scale image databases consisting of up to 10 million images. The main focus of this work is on feature bundling for object retrieval and logo recognition. We present two novel feature bundling techniques that aggregate multiple local features into a single visual description. In contrast to many other works, both approaches encode geometric information about the spatial layout of local features into the corresponding visual description itself. Therefore, these descriptions are highly distinctive and suitable for high-precision object retrieval. We demonstrate the use of both bundling techniques for logo recognition. Here, the recognition is performed by the retrieval of visually similar images from a database of reference images, making the recognition systems easily scalable to a large number of classes. The results show that our retrieval-based methods can successfully identify small objects such as logos with an extremely low false positive rate. In particular, our feature bundling techniques are beneficial because false positives are effectively avoided upfront due to the highly distinctive descriptions. We further demonstrate and thoroughly evaluate the use of our bundling technique based on min-Hashing for image and object retrieval. Compared to approaches based on conventional bag-of-words retrieval, it has much higher efficiency: the retrieved result lists are shorter and cleaner while recall is on equal level. The results suggest that this bundling scheme may act as pre-filtering step in a wide range of scenarios and underline the high effectiveness of this approach. Finally, we present a new variant for extremely fast re-ranking of retrieval results, which ranks the retrieved images according to the spatial consistency of their local features to those of the query image. The demonstrated method is robust to outliers, performs better than existing methods and allows to process several hundreds to thousands of images per second on a single thread

Natural scene classification, annotation and retrieval. Developing different approaches for semantic scene modelling based on Bag of Visual Words.

With the availability of inexpensive hardware and software, digital imaging has become an important medium of communication in our daily lives. A huge amount of digital images are being collected and become available through the internet and stored in various fields such as personal image collections, medical imaging, digital arts etc. Therefore, it is important to make sure that images are stored, searched and accessed in an efficient manner. The use of bag of visual words (BOW) model for modelling images based on local invariant features computed at interest point locations has become a standard choice for many computer vision tasks. Based on this promising model, this thesis investigates three main problems: natural scene classification, annotation and retrieval. Given an image, the task is to design a system that can determine to which class that image belongs to (classification), what semantic concepts it contain (annotation) and what images are most similar to (retrieval). This thesis contributes to scene classification by proposing a weighting approach, named keypoints density-based weighting method (KDW), to control the fusion of colour information and bag of visual words on spatial pyramid layout in a unified framework. Different configurations of BOW, integrated visual vocabularies and multiple image descriptors are investigated and analyzed. The proposed approaches are extensively evaluated over three well-known scene classification datasets with 6, 8 and 15 scene categories using 10-fold cross validation. The second contribution in this thesis, the scene annotation task, is to explore whether the integrated visual vocabularies generated for scene classification can be used to model the local semantic information of natural scenes. In this direction, image annotation is considered as a classification problem where images are partitioned into 10x10 fixed grid and each block, represented by BOW and different image descriptors, is classified into one of predefined semantic classes. An image is then represented by counting the percentage of every semantic concept detected in the image. Experimental results on 6 scene categories demonstrate the effectiveness of the proposed approach. Finally, this thesis further explores, with an extensive experimental work, the use of different configurations of the BOW for natural scene retrieval.Applied Science University in Jorda

Towards Universal Visual Vocabularies

Many content-based image mining systems extract local features from images to obtain an image description based on discrete feature occurrences. Such applications require a visual vocabulary also known as visual codebook or visual dictionary to discretize the extracted high-dimensional features to visual words in an efficient yet accurate way. Once such an application operates on images of a very specific domain the question arises if a vocabulary built from those domain-specific images needs to be used or if a "universal" visual vocabulary can be used instead. A universal visual vocabulary may be computed from images of a different domain once and then be re-used for various applications and other domains. We therefore evaluate several visual vocabularies from different image domains by determining their performance at pLSA-based image classification on several datasets. We empirically conclude that vocabularies suit our classification tasks equally well disregarding the image domain they were derived from