A survey of content-based image retrieval with high-level semantics

We explore the problem of classifying images by the object categories they contain in the case of a large number of object categories. To this end we combine three ingredients: (i) shape and appearance representations that support spatial pyramid matching over a region of interest. This generalizes the representation of Lazebnik et al., (2006) from an image to a region of interest (ROI), and from appearance (visual words) alone to appearance and local shape (edge distributions); (ii) automatic selection of the regions of interest in training. This provides a method of inhibiting background clutter and adding invariance to the object instance 's position; and (iii) the use of random forests (and random ferns) as a multi-way classifier. The advantage of such classifiers (over multi-way SVM for example) is the ease of training and testing. Results are reported for classification of the Caltech-101 and Caltech-256 data sets. We compare the performance of the random forest/ferns classifier with a benchmark multi-way SVM classifier. It is shown that selecting the ROI adds about 5% to the performance and, together with the other improvements, the result is about a 10% improvement over the state of the art for Caltech-256.

/pdf/image-classification-using-random-forests-and-ferns-4x16meo48x.pdf

Image Classification using Random Forests and Ferns

A large-scale study was set up aiming at the clarification of the influence of demographic and musical background on the semantic description of music. Our model for rating high-level music qualities distinguishes between affective-emotive, structural and kinaesthetic descriptors. The focus was on the understanding of the most important attributes of music in view of the development of efficient search and retrieval systems. We emphasized who the users of such systems are and how they describe their favorite music. Particular interest went to inter-subjective similarities among listeners. The results from our study suggest that gender, age, musical expertise, active musicianship, broadness of taste and familiarity with the music have an influence on the semantic description of music. © 2008 Wiley Periodicals, Inc.

https://biblio.ugent.be/publication/427156/file/6990792.pdf

How potential users of music search and retrieval systems describe the semantic quality of music

The PASCAL Visual Object Classes Challenge ran from February to March 2005. The goal of the challenge was to recognize objects from a number of visual object classes in realistic scenes (i.e. not pre-segmented objects). Four object classes were selected: motorbikes, bicycles, cars and people. Twelve teams entered the challenge. In this chapter we provide details of the datasets, algorithms used by the teams, evaluation criteria, and results achieved.

/pdf/the-2005-pascal-visual-object-classes-challenge-1g5j3ord3g.pdf

The 2005 PASCAL visual object classes challenge

The cumulative distribution transform and linear pattern classification

We present a novel model for object recognition and detection that follows the widely adopted assumption that objects in images can be represented as a set of loosely coupled parts. In contrast to former models, the presented method can cope with an arbitrary number of object parts. Here, the object parts are modelled by image patches that are extracted at each position and then efficiently stored in a histogram. In addition to the patch appearance, the positions of the extracted patches are considered and provide a significant increase in the recognition performance. Additionally, a new and efficient histogram comparison method taking into account inter-bin similarities is proposed. The presented method is evaluated for the task of radiograph recognition where it achieves the best result published so far. Furthermore it yields very competitive results for the commonly used Caltech object detection tasks.

/pdf/sparse-patch-histograms-for-object-classification-in-263pevnfp8.pdf

Sparse patch-histograms for object classification in cluttered images

We present an approach using Gaussian mixture models for part-based object recognition where spatial relationships of the parts are explicitly modeled and parameters of the generative model are tuned discriminatively. These extensions lead to great improvements of the classification accuracy. Furthermore we evaluate several improvements over our baseline system which incrementally improve the obtained results which compare favorable well to other published results for the three Caltech tasks and the PASCAL evaluation 05 tasks.

/pdf/patch-based-object-recognition-using-discriminatively-44ptrriy5k.pdf

Patch-based Object Recognition Using Discriminatively Trained Gaussian Mixtures

Data preprocessing and a careful selection of the training and classification method are key steps for building a predictive or classification model with high performance. Here, we present the winner approaches submitted to the 2005 GfKl Data Mining Competition. The task to be solved for the competition was the prediction of a possible liquidity crisis of a company. The binary classification was to be based on a set of 26 variables describing attributes of the companies with unknown semantics.

GfKl Data Mining Competition 2005: Predicting Liquidity Crises of Companies

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Sparse patch-histograms for object classification in cluttered images

Patch-based Object Recognition Using Discriminatively Trained Gaussian Mixtures

GfKl Data Mining Competition 2005: Predicting Liquidity Crises of Companies