Neti Neti: in search of deity
16 Dec 2012-pp 30
TL;DR: Empirical evaluations demonstrate that the proposed method of image-representation and rejection cascade improves the retrieval performance on this hard problem as compared to the baseline descriptors.
Abstract: A wide category of objects and scenes can be effectively searched and classified using the modern descriptors and classifiers. With the performance on many popular categories becoming satisfactory, we explore into the issues associated with much harder recognition problems.We address the problem of searching specific images in Indian stone-carvings and sculptures in an unsupervised setup. For this, we introduce a new dataset of 524 images containing sculptures and carvings of eight different Indian deities and three other subjects popular in the Indian scenario. We perform a thorough analysis to investigate various challenges associated with this task. A new image-representation is proposed using a sequence of discriminative patches mined in an unsupervised manner. For each image, these patches are identified based on their ability to distinguish the given image from the image most dissimilar to it. Then a rejection-based re-ranking scheme is formulated based on both similarity as well as dissimilarity between two images. This new scheme is experimentally compared with two baselines using state-of-the-art descriptors on the proposed dataset. Empirical evaluations demonstrate that our proposed method of image-representation and rejection cascade improves the retrieval performance on this hard problem as compared to the baseline descriptors.
References
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TL;DR: This paper presents a method for extracting distinctive invariant features from images that can be used to perform reliable matching between different views of an object or scene and can robustly identify objects among clutter and occlusion while achieving near real-time performance.
Abstract: This paper presents a method for extracting distinctive invariant features from images that can be used to perform reliable matching between different views of an object or scene. The features are invariant to image scale and rotation, and are shown to provide robust matching across a substantial range of affine distortion, change in 3D viewpoint, addition of noise, and change in illumination. The features are highly distinctive, in the sense that a single feature can be correctly matched with high probability against a large database of features from many images. This paper also describes an approach to using these features for object recognition. The recognition proceeds by matching individual features to a database of features from known objects using a fast nearest-neighbor algorithm, followed by a Hough transform to identify clusters belonging to a single object, and finally performing verification through least-squares solution for consistent pose parameters. This approach to recognition can robustly identify objects among clutter and occlusion while achieving near real-time performance.
46,906 citations
20 Jun 2005
TL;DR: It is shown experimentally that grids of histograms of oriented gradient (HOG) descriptors significantly outperform existing feature sets for human detection, and the influence of each stage of the computation on performance is studied.
Abstract: We study the question of feature sets for robust visual object recognition; adopting linear SVM based human detection as a test case. After reviewing existing edge and gradient based descriptors, we show experimentally that grids of histograms of oriented gradient (HOG) descriptors significantly outperform existing feature sets for human detection. We study the influence of each stage of the computation on performance, concluding that fine-scale gradients, fine orientation binning, relatively coarse spatial binning, and high-quality local contrast normalization in overlapping descriptor blocks are all important for good results. The new approach gives near-perfect separation on the original MIT pedestrian database, so we introduce a more challenging dataset containing over 1800 annotated human images with a large range of pose variations and backgrounds.
31,952 citations
"Neti Neti: in search of deity" refers methods in this paper
...We represent both these images using a collection of square patches around interest points described using the Histogram of Oriented Gradients (hog) descriptor [6]....
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TL;DR: The state-of-the-art in evaluated methods for both classification and detection are reviewed, whether the methods are statistically different, what they are learning from the images, and what the methods find easy or confuse.
Abstract: The Pascal Visual Object Classes (VOC) challenge is a benchmark in visual object category recognition and detection, providing the vision and machine learning communities with a standard dataset of images and annotation, and standard evaluation procedures. Organised annually from 2005 to present, the challenge and its associated dataset has become accepted as the benchmark for object detection.
This paper describes the dataset and evaluation procedure. We review the state-of-the-art in evaluated methods for both classification and detection, analyse whether the methods are statistically different, what they are learning from the images (e.g. the object or its context), and what the methods find easy or confuse. The paper concludes with lessons learnt in the three year history of the challenge, and proposes directions for future improvement and extension.
15,935 citations
"Neti Neti: in search of deity" refers background in this paper
...In object classification problems such as classification on the pascal dataset [9], context knowledge (water) can be very helpful in identifying an object (ship vs....
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...cats and dogs), simple BoW models outperform the state-of-the-art Deformable Part Model (dpm) [9]....
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01 Jan 2011
TL;DR: The Scale-Invariant Feature Transform (or SIFT) algorithm is a highly robust method to extract and consequently match distinctive invariant features from images that can then be used to reliably match objects in diering images.
Abstract: The Scale-Invariant Feature Transform (or SIFT) algorithm is a highly robust method to extract and consequently match distinctive invariant features from images. These features can then be used to reliably match objects in diering images. The algorithm was rst proposed by Lowe [12] and further developed to increase performance resulting in the classic paper [13] that served as foundation for SIFT which has played an important role in robotic and machine vision in the past decade.
14,708 citations
TL;DR: An object detection system based on mixtures of multiscale deformable part models that is able to represent highly variable object classes and achieves state-of-the-art results in the PASCAL object detection challenges is described.
Abstract: We describe an object detection system based on mixtures of multiscale deformable part models. Our system is able to represent highly variable object classes and achieves state-of-the-art results in the PASCAL object detection challenges. While deformable part models have become quite popular, their value had not been demonstrated on difficult benchmarks such as the PASCAL data sets. Our system relies on new methods for discriminative training with partially labeled data. We combine a margin-sensitive approach for data-mining hard negative examples with a formalism we call latent SVM. A latent SVM is a reformulation of MI--SVM in terms of latent variables. A latent SVM is semiconvex, and the training problem becomes convex once latent information is specified for the positive examples. This leads to an iterative training algorithm that alternates between fixing latent values for positive examples and optimizing the latent SVM objective function.
10,501 citations
"Neti Neti: in search of deity" refers background or methods in this paper
...Along with these developments, significant efforts have been put into developing new models that complement the modern descriptors, and are capable of modelling the shape and relative position of the parts of objects [10]....
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...For each cell, a hog descriptor is computed using the method and code from [10]....
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...(ii) Though there exist one or more distinctive parts for most of the categories, they themselves might be quite flexible, articulated and even occluded/eroded due to which locating them using dpm [10] becomes non-trivial....
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