The Bag-of-Words (BoW) representation is widely used in computer vision. The size of the codebook impacts the time and space complexity of the applications that use BoW. Thus, given a training set for a particular computer vision task, a key problem is pruning a large codebook to select only a subset of visual words. Evaluating possible selections of words to be included in the pruned codebook can be computationally prohibitive; in a brute-force scheme, evaluating each pruned codebook requires re-coding of all features extracted from training images to words in the candidate codebook and then re-pooling the words to obtain a representation of each image, e.g., histogram of visual word frequencies. In this paper, a method is proposed that selects and evaluates a subset of words from an initially large codebook, without the need for re-coding or re-pooling. Formulations are proposed for two commonly-used schemes: hard and soft (kernel) coding of visual words with average-pooling. The effectiveness of these formulations is evaluated on the 15 Scenes and Caltech 10 benchmarks.
Borrowing ideas from the document retrieval domain @cite_11 , traditional codeword selection methods use criteria such as the term frequency, @math statistic, mutual information and learned SVM weights to select the most discriminative codewords @cite_4 @cite_13 . Winn and Minka @cite_2 propose to merge visual words textons with respect to a probabilistic measure defined on the altered representations. Doing so they aim to find dimensions in the original representation to merge that presumably correspond to the same textures but are captured under different lighting or viewing angles. Similarly, @cite_17 merges pairs of visual words based on a mutual information measure. Wang @cite_7 employs a boosting mechanism where each weak classifier is associated with a codeword and selection of weak classifiers in the procedure naturally results in the selection of the most discriminative codewords. Zhang, et al @cite_20 considers an unsupervised scheme in which the visual words are selected by constructing a ridge regression formulation.
@cite_11 @cite_4 @cite_7 @cite_2 @cite_13 @cite_20 @cite_17
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Based on keypoints extracted as salient image patches, an image can be described as a "bag of visual words" and this representation has been used in scene classification. The choice of dimension, selection, and weighting of visual words in this representation is crucial to the classification performance but has not been thoroughly studied in previous work. Given the analogy between this representation and the bag-of-words representation of text documents, we apply techniques used in text categorization, including term weighting, stop word removal, feature selection, to generate image representations that differ in the dimension, selection, and weighting of visual words. The impact of these representation choices to scene classification is studied through extensive experiments on the TRECVID and PASCAL collection. This study provides an empirical basis for designing visual-word representations that are likely to produce superior classification performance. In patch-based object recognition, there are two important issues on the codebook generation: (I) resolution: a coarse codebook lacks sufficient discriminative power, and an over-fine one is sensitive to noise; (2) codeword selection: non-discriminative codewords not only increase the codebook size, but also can hurt the recognition performance. To achieve a discriminative codebook for better recognition, this paper argues that these two issues are strongly related and should be solved as a whole. In this paper, a multi-resolution codebook is first designed via hierarchical clustering. With a reasonable size, it includes all of the codewords which cross a large number of resolution levels. More importantly, it forms a diverse candidate codeword set that is critical to codeword selection. A Boosting feature selection approach is modified to select the discriminative codewords from this multi-resolution code-book. By doing so, the obtained codebook is composed of the most discriminative codewords culled from different levels of resolution. Experimental study demonstrates the better recognition performance attained by this codebook. This paper presents a new algorithm for the automatic recognition of object classes from images (categorization). Compact and yet discriminative appearance-based object class models are automatically learned from a set of training images. The method is simple and extremely fast, making it suitable for many applications such as semantic image retrieval, Web search, and interactive image editing. It classifies a region according to the proportions of different visual words (clusters in feature space). The specific visual words and the typical proportions in each object are learned from a segmented training set. The main contribution of this paper is twofold: i) an optimally compact visual dictionary is learned by pair-wise merging of visual words from an initially large dictionary. The final visual words are described by GMMs. ii) A novel statistical measure of discrimination is proposed which is optimized by each merge operation. High classification accuracy is demonstrated for nine object classes on photographs of real objects viewed under general lighting conditions, poses and viewpoints. The set of test images used for validation comprise: i) photographs acquired by us, ii) images from the Web and iii) images from the recently released Pascal dataset. The proposed algorithm performs well on both texture-rich objects (e.g. grass, sky, trees) and structure-rich ones (e.g. cars, bikes, planes) Bag of features (BoF) representation has attracted an increasing amount of attention in large scale image processing systems. BoF representation treats images as loose collections of local invariant descriptors extracted from them. The visual codebook is generally constructed by using an unsupervised algorithm such as K-means to quantize the local descriptors into clusters. Images are then represented by the frequency histograms of the codewords contained in them. To build a compact and discriminative codebook, codeword selection has become an indispensable tool. However, most of the existing codeword selection algorithms are supervised and the human labeling may be very expensive. In this paper, we consider the problem of unsupervised codeword selection, and propose a novel algorithm called Discriminative Codeword Selection (DCS). Motivated from recent studies on discriminative clustering, the central idea of our proposed algorithm is to select those codewords so that the cluster structure of the image database can be best respected. Specifically, a multi-output linear function is fitted to model the relationship between the data matrix after codeword selection and the indicator matrix. The most discriminative codewords are thus defined as those leading to minimal fitting error. Experiments on image retrieval and clustering have demonstrated the effectiveness of the proposed method. We present an approach to determine the category and location of objects in images. It performs very fast categorization of each pixel in an image, a brute-force approach made feasible by three key developments: First, our method reduces the size of a large generic dictionary (on the order of ten thousand words) to the low hundreds while increasing classification performance compared to k-means. This is achieved by creating a discriminative dictionary tailored to the task by following the information bottleneck principle. Second, we perform feature-based categorization efficiently on a dense grid by extending the concept of integral images to the computation of local histograms. Third, we compute SIFT descriptors densely in linear time. We compare our method to the state of the art and find that it excels in accuracy and simplicity, performing better while assuming less.