Bag Of Keypoints Research Articles

Integrative blockwise sparse analysis for tissue characterization and classification.

The topic of sparse representation of samples in high dimensional spaces has attracted growing interest during the past decade. In this work, we develop sparse representation-based methods for classification of clinical imaging patterns into healthy and diseased states. We propose a spatial block decomposition method to address irregularities of the approximation problem and to build an ensemble of classifiers that we expect to yield more accurate numerical solutions than conventional sparse analyses of the complete spatial domain of the images. We introduce two classification decision strategies based on maximum a posteriori probability (BBMAP), or a log likelihood function (BBLL) and an approach to adjusting the classification decision criteria. To evaluate the performance of the proposed approach we used cross-validation techniques on imaging datasets with disease class labels. We first applied the proposed approach to diagnosis of osteoporosis using bone radiographs. In this problem we assume that changes in trabecular bone connectivity can be captured by intensity patterns. The second application domain is separation of breast lesions into benign and malignant categories in mammograms. The object classes in both of these applications are not linearly separable, and the classification accuracy may depend on the lesion size in the second application. Our results indicate that the proposed integrative sparse analysis addresses the ill-posedness of the approximation problem and produces very good class separation for trabecular bone characterization and for breast lesion characterization. Our approach yields higher classification rates than conventional sparse classification and previously published convolutional neural networks (CNNs) that we fine-tuned for our datasets, or utilized for feature extraction. The BBLL technique also produced higher classification rates than learners using hand-crafted texture features, and the Bag of Keypoints, which is a sophisticated patch-based method. Furthermore, our comparative experiments showed that the BBLL function may yield more accurate classification than BBMAP, because BBLL accounts for possible estimation bias.

BM25 With Exponential IDF for Instance Search

This paper deals with a novel concept of an exponential IDF in the BM25 formulation and compares the search accuracy with that of the BM25 with the original IDF in a content-based video retrieval (CBVR) task. Our video retrieval method is based on a bag of keypoints (local visual features) and the exponential IDF estimates the keypoint importance weights more accurately than the original IDF. The exponential IDF is capable of suppressing the keypoints from frequently occurring background objects in videos, and we found that this effect is essential for achieving improved search accuracy in CBVR. Our proposed method is especially designed to tackle instance video search, one of the CBVR tasks, and we demonstrate its effectiveness in significantly enhancing the instance search accuracy using the TRECVID2012 video retrieval dataset.

An effective biomedical image retrieval framework in a fuzzy feature space employing Phase Congruency and GeoSOM

This paper presents a detailed study about a biomedical image retrieval framework by extracting Phase Congruency (PC) features from L*a*b* triplets of images (query, target) and representing them in fuzzy feature space. These features correspond to an edge-corner map of the given image. The resulting map is then processed by Scale Invariant Feature Transform (SIFT) to derive keypoints, that are invariant to affine transformations. The ensuing features were vector quantized to build a codebook of keypoints. The codebook was produced using a Spherical Self-Organizing Map (SOM) built with a geodesic data structure termed as GeoSOM. Then keypoints of the query image are mapped with the codebook and their occurrences are counted to formulate a histogram termed as Phase Congruency-based Bag of Keypoints (PC-BoK). This histogram is generated offline for target images and a similarity measure was performed with the query image to yield the nearest match based on a global fuzzy membership function. Exhaustive experiments of the proposed framework named as BIRS (Biomedical Image Retrieval System) were performed on a diverse medical image collection (NBIA, MESSIDOR, DRIVE). Finally, performance of BIRS demonstrates the advantage of the proposed image representation approach in terms of Precision (P)–Recall (R) parameters. Furthermore relative comparison of the proposed scheme with existing feature descriptors depicts improved P–R values. The proposed feature extraction and representation scheme was also robust against quantization errors.

Using Local-Based Harris-PHOG Features in a Combination Framework for Human Action Recognition

Local-based approaches have recently shown a great promise for computer vision tasks and local space-time features have become a popular video representation for human action recognition. In this paper, we focus on recognizing the action of a person based on the appearance and motion information by constructing spatio-temporal features in the “bag of keypoints” paradigm. We evaluate the performance of some local appearance detectors and descriptors along with the Histogram of Oriented Optical Flow (HOOF) as a motion descriptor. These features consist of SIFT, SURF and Harris-PHOG, each of which is concatenated with the HOOF of interest points. Although no single type of these features is optimal for all needs in the recognition task, in this paper, we present a feature combination representation to improve the recognition rate. In this method, we assume an image representation based on a single feature and extend it to the case of multiple features from several descriptors. Such a representation for merging features has high flexibility such that other descriptors yielding other feature vectors can be easily incorporated. We use Support Vector Machine as the classifier and evaluate results using two kinds of kernels, the polynomial kernel and the radial basis function kernel. The proposed methods achieve higher or similar accuracies compared with several state-of-the-art categorization methods on the challenging benchmark dataset of KTH.

12th Century Ancient Tamil Character Recognition From Temple Wall Inscriptions

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HOGとBag of Keypointsを用いた工作機械用制御盤内における物体認識に関する研究

HOGとBag of Keypointsを用いた工作機械用制御盤内における物体認識に関する研究

HOG と Bag of Keypoints を用いた工作機械用制御盤内における物体認識

HOG と Bag of Keypoints を用いた工作機械用制御盤内における物体認識

2P1-E19 ロボットビジョンによる室内環境の認識 : SIFT特徴量とエッジ情報を用いたオフィス機器の画像探索と認識

For the purpose of simultaneous localization and mapping (SLAM) of an indoor mobile robot, a method to process only one monocular image (640×480) of the whole view of environment is proposed. This method imitates that a human can grasp momentarily the situation of a room, i.e., the layout of objects/obstacles. Specific object recognition for desks with several view angles are dealt with. The proposed method is concretely organized as follows: 1) Bag of Keypoints is applied, then the existence of object on input image is judged, 2) If object existence is verified, its angle is detected by Bag of Keypoints, 3) The candidates for the projection from template image to input image is obtained using Scale Invariant Feature Transform (SIFT) and edge information, the validity of which is decided by AdaBoost classifier based on various image features, such as Hu moment, Haar-like features, etc.

Color Image Classification Using Block Matching and Learning

In this paper, we propose block matching and learning for color image classification. In our method, training images are partitioned into small blocks. Given a test image, it is also partitioned into small blocks, and mean-blocks corresponding to each test block are calculated with neighbor training blocks. Our method classifies a test image into the class that has the shortest total sum of distances between mean blocks and test ones. We also propose a learning method for reducing memory requirement. Experimental results show that our classification outperforms other classifiers such as support vector machine with bag of keypoints.