Simultaneously Discovering and Localizing Common Objects in Wild Images

How to accurately and efficiently localize objects in images is a challenging computer vision problem. In this article, a novel top-down fine-grained saliency object localization method based on deep-learned features is proposed, which can localize the same object in input image as the query image. The query image and its three subsample images are used as top-down cues to guide saliency detection. We ameliorate Convolutional Neural Network (CNN) using the fast VGG network (VGG-f) and retrained on the Pascal VOC 2012 dataset. Experiment on the FiFA dataset demonstrates that the proposed algorithm can effectively localize the saliency region and find the same object (human face) as the query. Experiments on the David1 and Face1 sequences conclusively prove that the proposed algorithm is able to effectively deal with different challenging factors including appearance and scale variations, shape deformation and partial occlusion.

Image Indexing and Retrieval Using Object-based Point Feature Maps

Indexing and retrieval of images by spatial constraints

Change detection, or anomaly detection, from street-view images acquired by an autonomous robot at multiple different times, is a major problem in robotic mapping and autonomous driving. Formulation as an image comparison task, which operates on a given pair of query and reference images is common to many existing approaches to this problem. Unfortunately, providing relevant reference images is not straightforward. In this paper, we propose a novel formulation for change detection, termed compressive change retrieval, which can operate on a query image and similar reference images retrieved from the web. Compared to previous formulations, there are two sources of difficulty. First, the retrieved reference images may frequently contain non-relevant reference images, because even state-of-the-art place-recognition techniques suffer from retrieval noise. Second, image comparison needs to be conducted in a compressed domain to minimize the storage cost of large collections of street-view images. To address the above issues, we also present a practical change detection algorithm that uses compressed bag-of-words (BoW) image representation as a scalable solution. The results of experiments conducted on a practical change detection task, "moving object detection (MOD)," using the publicly available Malaga dataset validate the effectiveness of the proposed approach.

Template matching is the process of searching the present and the location of a reference image or an object in a scene image. Template matching is a classical problem in a scene analysis: given a reference image of an object, decide whether that object exists in a scene image under analysis, and find its location if it does. The template matching process involves cross-correlating the template with the scene image and computing a measure of similarity between them to determine the displacement. The conventional matching method used the spatial cross-correlation process which is computationally expensive. Some algorithms are proposed for this speed problem, such as pyramid algorithm, but it still can't reach the real-time for bigger model image. Moreover, the cross-correlation algorithm can't be effective when the object in the image is rotated. Therefore, the conventional algorithms can't be used for practical purpose. In this paper, an algorithm for a rotation invariant template matching method based on different value circular projection target tracking algorithm is proposed. This algorithm projects the model image as circular and gets the radius and the sum of the same radius pixel value. The sum of the same radius pixel value is invariable for the same image and the any rotated angle image. Therefore, this algorithm has the rotation invariant property. In order to improve the matching speed and get the illumination invariance, the different value method is combined with circular projection algorithm. This method computes the different value between model image radius pixel sum and the scene image radius pixel sum so that it gets the matching result. The pyramid algorithm also is been applied in order to improve the matching speed. The high speed hardware system also is been design in order to meet the real time requirement of target tracking system. The results show that this system has the good rotate invariance and real-time property.

Unsupervised object discovery aims to localize objects in images, while removing the dependence on annotations required by most deep learning-based methods. To address this problem, we propose a fully unsupervised, bottom-up approach, for multiple objects discovery. The proposed approach is a two-stage framework. First, instances of object parts are segmented by using the intra-image similarity between self-supervised local features. The second step merges and filters the object parts to form complete object instances. The latter is performed by two CNN models that capture semantic information on objects from the entire dataset. We demonstrate that the pseudo-labels generated by our method provide a better precision-recall trade-off than existing single and multiple objects discovery methods. In particular, we provide state-of-the-art results for both unsupervised class-agnostic object detection and unsupervised image segmentation.

Rotation invariant HOG for object localization in web images

Detection of articulated objects in images, including location and state, is an important and challenging task in many object tracking applications. Image edges have proven to be a key feature, although their quality is influenced by many factors. In this paper, we propose a novel edge gradient-based template matching method for object detection. In contrast to other methods, ours does not perform any binarization or discretization during the online matching. This is facilitated by a new continuous edge gradient similarity measure. Its main components are a novel edge gradient operator, which is applied to query and template images, and the formulation as a convolution, which can be computed very efficiently in Fourier space. Our method consists of a preprocessing stage for the template images, a simple preprocessing of the query image, and our similarity measure between template and query image, which yields a confidence map for the query image. The resulting confidence map can be used for the final object localization. We compared our method to a state-of-the-art chamfer-based matching method. The results demonstrate that our method is much more robust against weak edge response and yields much better confidence maps with fewer maxima that are also more significant. In addition, our method lends itself well to efficient implementation on GPUs: at a query image resolution of 320× 256 and a template resolution of 80× 80 we can generate about 330 confidence maps per second.

Relevance feedback is an important mechanism for narrowing the semantic gap in content-based image retrieval and the process involves the user labeling positive and negative images. Very often, it is some specific objects or regions in the positive feedback images that the user is really interested in rather than the entire image. This paper presents a hierarchical graphical model for automatically extracting objects and regions that the user is interested in from the positive images which in turn are used to derive features that better reflect the user’s feedback intentions for improving interactive image retrieval. The novel hierarchical graphical model embeds image formation prior, user intention prior and statistical prior in its edges and uses a max-flow/min-cut method to simultaneously segment all positive feedback images into user interested and user uninterested regions. An important innovation of the graphical model is the introduction of a layer of visual appearance prototype nodes to incorporate user intention and form bridges linking similar objects in different images. This architecture not only makes it possible to use all feedback images to obtain more robust user intention prior thus improving the object segmentation results and in turn enhancing the retrieval performance, but also greatly reduces the complexity of the graph and the computational cost. Experimental results are presented to demonstrate the effectiveness of the new method.

In this paper, we consider the task of discovering the common objects in images. Initially, object candidates are generated in each image and an undirected weighted graph is constructed over all the candidates. Each candidate serves as a node in the graph while the weight of the edge describes the similarity between the corresponding pair of candidates. The problem is then expressed as a search for the Maximum Weight Clique (MWC) in this graph. The MWC corresponds to a set of object candidates sharing maximal mutual similarity, and each node in the MWC represents a discovered common object across the images. Since the problem of finding the MWC is NP-hard, most research of the MWC problem focuses on developing various heuristics for finding good cliques within a reasonable time limit. We utilize a recently very popular class of heuristics called local search methods. They search for the MWC directly in the discrete domain of the solution space. The proposed approach is evaluated on the PASCAL VOC image dataset and the YouTube-Objects video dataset, and it demonstrates superior performance over recent state-of-the-art approaches.

Detection of particular meaningful objects in images is of great importance in many fields, including image retrieval or image quality analysis. In this contribution, eleven frequently observed objects (areas) in natural images are learned and detected. The presented algorithm is based on region merging and Bayesian decision theory. The main goal is not perfect recognition, since for that purpose it is necessary to use higher-level knowledge about the image content. Merging of segments proceeds only up to a reliable point, not to merge different categories. Unique merging criteria combine the values of probabilities attached to the segments for all the most likely categories, color and texture features and information about edges. Results are demonstrated on a few images and discussed.

One of the techniques in region based image retrieval (RBIR) is comparing the global feature of an entire image and the local feature of image’s sub-block in query and database image. The determined sub-block must be able to detect an object with varying sizes and locations. So the sub-block with flexible size and location is needed. We propose a new method for local feature extraction by determining the flexible size and location of sub-block based on the transition region in region based image retrieval. Global features of both query and database image are extracted using invariant moment. Local features in database and query image are extracted using hue, saturation, and value (HSV) histogram and local binary patterns (LBP). There are several steps to extract the local feature of sub-block in the query image. First, preprocessing is conducted to get the transition region, then the flexible sub-block is determined based on the transition region. Afterward, the local feature of sub-block is extracted. The result of this application is the retrieved images ordered by the most similar to the query image. The local feature extraction with the proposed method is effective for image retrieval with precision and recall value are 57%.

Due to the huge growth of the World Wide Web, medical images are now available in large numbers in online repositories, and there exists the need to retrieval the images through automatically extracting visual information of the medical images, which is commonly known as content-based image retrieval (CBIR). Since each feature extracted from images just characterizes certain aspect of image content, multiple features are necessarily employed to improve the retrieval performance. Meanwhile, experiments demonstrate that a special feature is not equally important for different image queries. Most of existed feature fusion methods for image retrieval only utilize query independent feature fusion or rely on explicit user weighting. In this paper, we present a novel query dependent feature fusion method for medical image retrieval based on one class support vector machine. Having considered that a special feature is not equally important for different image queries, the proposed query dependent feature fusion method can learn different feature fusion models for different image queries only based on multiply image samples provided by the user, and the learned feature fusion models can reflect the different importances of a special feature for different image queries. The experimental results on the IRMA medical image collection demonstrate that the proposed method can improve the retrieval performance effectively and can outperform existed feature fusion methods for image retrieval.

Of great importance are the tasks of recognizing objects of a given shape and color on complex background in conditions of interference with high accuracy. In this regard, the development of effective algorithms for selecting objects that reduce the influence of noise component and reduce computational costs of performing operations in which the result will be suitable for subsequent analysis within the framework of the tasks being solved is an urgent task. This paper presents a model of objects and a method based on radial symmetry transformation for the problems of recognizing round-shaped colored objects in color images. Examples of such objects include the pupils of the eyes, traffic lights, car lights and light markers. The aim of the work is to increase the accuracy of detecting round-shaped colored objects in the images under interference conditions using modified radial symmetry transformation. The paper describes a model and a method for detecting objects in images, presents the results of numerical experiments that allow us to estimate the limits of applicability of the proposed method.

As the use of digital media increases, effective retrieval and management techniques become more important. In order to extract useful information from this huge amount of data, many content based image retrieval (CBIR) systems have been developed. A typical CBIR system captures image features such as color, texture, or shape of objects in the query image and tries to retrieve images from the database with similar features. The main advantage of CBIR systems with relevance feedback is that these systems take into account the gap between the high-level concepts and low-level features and subjectivity of human perception of visual content. In this paper, a CBIR system based on color histogram has been presented and a new approach for image storage and retrieval called association-based image retrieval (ABIR) has been studied. The results of both systems have been shown.