Attention to distinguishing features in object recognition: An interactive-iterative framework

In picture processing, picture segmentation, object classification and recognition is an impressive concept in present days because of increasing computer vision applications in real time applications. Based on required application, divide a picture into particular relevant clusters based on equivalent partitioning. Object identification is a minimum work to identify objects in a given picture or video with pixel values. The main representation of object classification from a given picture is to detect picture instances with respect to categorization of object. Object recognition is a complex task, when the pictures are poor quality, occlusion, and noise and in picture with background cutter sequences, this becomes more and efficient challenging task in picture processing. To appear object categorization and recognition effectively in pictures, there are several methods and approaches were introduced to solve object representation effectively. In this paper we review the different techniques of contextual feature extraction with respect to picture information of object classification, categorization and recognition of scalability and optimizations in real time picture processing applications. Our research gives better review of different approaches in picture categorization.

Behavioral studies in humans indicate that peripheral vision can do object recognition to some extent. Moreover, recent studies have shown that some information from brain regions retinotopic to visual periphery is somehow fed back to regions retinotopic to the fovea and disrupting this feedback impairs object recognition in human. However, it is unclear to what extent the information in visual periphery contributes to human object categorization. Here, we designed two series of rapid object categorization tasks to first investigate the performance of human peripheral vision in categorizing natural object images at different eccentricities and abstraction levels (superordinate, basic, and subordinate). Then, using a delayed foveal noise mask, we studied how modulating the foveal representation impacts peripheral object categorization at any of the abstraction levels. We found that peripheral vision can quickly and accurately accomplish superordinate categorization, while its performance in finer categorization levels dramatically drops as the object presents further in the periphery. Also, we found that a 300-ms delayed foveal noise mask can significantly disturb categorization performance in basic and subordinate levels, while it has no effect on the superordinate level. Our results suggest that human peripheral vision can easily process objects at high abstraction levels, and the information is fed back to foveal vision to prime foveal cortex for finer categorizations when a saccade is made toward the target object.

Object recognition is a key function in both human and machine vision. While brain decoding of seen and imagined objects has been achieved, the prediction is limited to training examples. We present a decoding approach for arbitrary objects using the machine vision principle that an object category is represented by a set of features rendered invariant through hierarchical processing. We show that visual features, including those derived from a deep convolutional neural network, can be predicted from fMRI patterns, and that greater accuracy is achieved for low-/high-level features with lower-/higher-level visual areas, respectively. Predicted features are used to identify seen/imagined object categories (extending beyond decoder training) from a set of computed features for numerous object images. Furthermore, decoding of imagined objects reveals progressive recruitment of higher-to-lower visual representations. Our results demonstrate a homology between human and machine vision and its utility for brain-based information retrieval.

As an important branch of artificial intelligence, computer vision plays a huge role in the rapid development of artificial intelligence. From a biological point of view, in the acquisition and processing of information, vision is much more important than hearing, touch, etc., because 70% of the human cerebral cortex is processing visual information. Therefore, advances in computer vision technology are critical to the development of artificial intelligence that is designed to allow machines to think and handle things like humans. The acquisition and processing of visual information has always been the focus of computer vision research, and it is also difficult. The main problem of traditional computer vision technology in the processing of visual information is that the extracted image features are less discriminative, the generalization ability of image features in complex background scenes is insufficient, and the recognition ability on object recognition is poor. In response to these problems, based on the visual neural mechanism, this paper establishes an appropriate computer model for the neuronal cells in the human primary visual cortex, models the recognition response mechanism of the visual ventral system, and performs image feature extraction on the training samples. And object recognition. The results show that compared with the traditional methods, the proposed method effectively improves the discrimination of image features, and the image features extracted under complex background scenes have good generalization ability. On this basis, the training samples can be effectively recognized. The results show that the model based on the visual neural mechanism, the recognition of the edge, orientation and contour of the training sample show the advantages of the biological vision mechanism in object recognition.

We introduce a novel set of features for robust object recognition. Each element of this set is a complex feature obtained by combining position- and scale-tolerant edge-detectors over neighboring positions and multiple orientations. Our system's architecture is motivated by a quantitative model of visual cortex. We show that our approach exhibits excellent recognition performance and outperforms several state-of-the-art systems on a variety of image datasets including many different object categories. We also demonstrate that our system is able to learn from very few examples. The performance of the approach constitutes a suggestive plausibility proof for a class of feedforward models of object recognition in cortex.

Gabor Wavelet Selection and SVM Classification for Object Recognition

There is substantial evidence for individual differences in personality and cognitive abilities, but we lack clear intuitions about individual differences in visual abilities. Previous work on this topic has typically compared performance with only 2 categories, each measured with only 1 task. This approach is insufficient for demonstration of domain-general effects. Most previous work has used familiar object categories, for which experience may vary between participants and categories, thereby reducing correlations that would stem from a common factor. In Study 1, we adopted a latent variable approach to test for the first time whether there is a domain-general object recognition ability, o. We assessed whether shared variance between latent factors representing performance for each of 5 novel object categories could be accounted for by a single higher-order factor. On average, 89% of the variance of lower-order factors denoting performance on novel object categories could be accounted for by a higher-order factor, providing strong evidence for o. Moreover, o also accounted for a moderate proportion of variance in tests of familiar object recognition. In Study 2, we assessed whether the strong association across categories in object recognition is due to third-variable influences. We find that o has weak to moderate associations with a host of cognitive, perceptual, and personality constructs and that a clear majority of the variance in and covariance between performance on different categories is independent of fluid intelligence. This work provides the first demonstration of a reliable, specific, and domain-general object recognition ability, and suggest a rich framework for future work in this area. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Object recognition is of fundamental importance in computer vision. In a few years, pedestrian detection, car detection, and more generally scene recognition will likely be reliable enough to allow fully-automated car navigation, and the human driver will be relegated to the back seat to sip his coffee. In this thesis we are interested in recognizing individual objects and categories. In order to reduce the volume of information one has to process, images are characterized by sets of features. These features, also called interest points, are targeted at image locations with high local information content. Various systems for detecting interest points and for describing the local image appearance near these points, have been proposed in the last two decades. We investigate which combinations from this plethora of detectors and descriptors, are most suited for object recognition tasks. On to the problem of object recognition, we are first interested in recognizing individual objects. In a few years, one can imagine that customers in shops, will take with their cell phone a picture of a product that looks interesting, send it to a remote server with a huge database of individual objects, and get back information about that specific product. We propose a system for individual object recognition, inspired from previous work on coarse-to-fine recognition. All steps of the recognition process are translated into principled probabilistic terms, which allows us to outperform a state-of-the-art commercial system for individual recognition. Regarding categories, faces are probably the category that has received the most attention in computer vision literature. Here we propose a system to recognize images of the same individual in large databases of images. This can be of high interest when looking for images of a given person over the internet. Our method's advantage is that it works on real-world images, as opposed to the face databases from the literature, collected in laboratories with controlled lighting, pose and background conditions. Finally, we are interested in recognition of object categories in general. Using support vector machines for the classification task, we propose a features-based kernel that improves recognition performance on object categories.

A novel supervised approach to learning efficient kernel descriptors for high accuracy object recognition

The SIFT algorithm (Scale Invariant Feature Transform) proposed by Lowe [1] is an approach for extracting distinctive invariant features from images. It has been successfully applied to a variety of computer vision problems based on feature matching including object recognition, pose estimation, image retrieval and many others. However, in real-world applications there is still a need for improvement of the algorithm’s robustness with respect to the correct matching of SIFT features. In this paper, an improvement of the original SIFT algorithm providing more reliable feature matching for the purpose of object recognition is proposed. The main idea is to divide the features extracted from both the test and the model object image into several sub-collections before they are matched. The features are divided into several sub-collections considering the features arising from different octaves, that is from different frequency domains. The SIFT algorithm (Scale Invariant Feature Transform) proposed by Lowe [1] is an approach for extracting distinctive invariant features from images. It has been successfully applied to a variety of computer vision problems based on feature matching including object recognition, pose estimation, image retrieval and many others. However, in real-world applications there is still a need for improvement of the algorithm’s robustness with respect to the correct matching of SIFT features. In this paper, an improvement of the original SIFT algorithm providing more reliable feature matching for the purpose of object recognition is proposed. The main idea is to divide the features extracted from both the test and the model object image into several sub-collections before they are matched. The features are divided into several sub-collections considering the features arising from different octaves, that is from different frequency domains.

Scene recognition with RGB images has been extensively studied and has reached very remarkable recognition levels, thanks to convolutional neural networks (CNN) and large scene datasets. In contrast, current RGB-D scene data is much more limited, so often leverages RGB large datasets, by transferring pretrained RGB CNN models and fine-tuning with the target RGB-D dataset. However, we show that this approach has the limitation of hardly reaching bottom layers, which is key to learn modality-specific features. In contrast, we focus on the bottom layers, and propose an alternative strategy to learn depth features combining local weakly supervised training from patches followed by global fine tuning with images. This strategy is capable of learning very discriminative depth-specific features with limited depth images, without resorting to Places-CNN. In addition we propose a modified CNN architecture to further match the complexity of the model and the amount of data available. For RGB-D scene recognition, depth and RGB features are combined by projecting them in a common space and further leaning a multilayer classifier, which is jointly optimized in an end-to-end network. Our framework achieves state-of-the-art accuracy on NYU2 and SUN RGB-D in both depth only and combined RGB-D data.

Object recognition is a crucial brain function that involves a complex interplay between various brain regions. However, the behavioral relevance of functional interactions between these regions remains largely unexplored. In this study, we examined the functional interactions between different brain regions during object recognition using intracranial electrocorticography (ECoG) recordings in subjects diagnosed with pharmacologically intractable epilepsy. We computed the phase locking value (PLV) between different brain areas and its category selectivity, and assessed its behavioral relevance by comparing correctly and incorrectly performed trials. Our results revealed that phase locking between brain regions varies across different object categories and that this variability significantly influences the perceptual behavior of subjects. Importantly, we found that the behavioral relevance of these interactions is spatially organized, with the high behaviorally relevant connections being longer for the frontal lobe and shorter for the occipital lobe. These findings underscore the unique roles of different brain areas in object recognition and pave the way for more nuanced explorations of the interplay between brain regions in object recognition and other cognitive functions.

The inter-class face classification problem is more reasonable than the intra-class classification problem. To address this issue, we have carried out empirical research on classifying Indian people to their geographical regions. This work aimed to construct a computational classification model for classifying Indian regional face images acquired from south and east regions of India, referring to human vision. We have created an Automated Human Intelligence System (AHIS) to evaluate human visual capabilities. Analysis of AHIS response showed that face shape is a discriminative feature among the other facial features. We have developed a modified convolutional neural network to characterize the human vision response to improve face classification accuracy. The proposed model achieved mean F1 and Matthew Correlation Coefficient (MCC) of 0.92 and 0.84, respectively, on the validation set, outperforming the traditional Convolutional Neural Network (CNN). The CNN-Contoured Face (CNN-FC) model is developed to train contoured face images to investigate the influence of face shape. Finally, to cross-validate the accuracy of these models, the traditional CNN model is trained on the same dataset. With an accuracy of 92.98%, the Modified-CNN (M-CNN) model has demonstrated that the proposed method could facilitate the tangible impact in intra-classification problems. A novel Indian regional face dataset is created for supporting this supervised classification work, and it will be available to the research community.

For hyperspectral classification, mixed pixels are usually the biggest reason to reduce the classification accuracy. To solve the problem, we apply the multi-label classification technique to the hyperspectral image classification. The approach Label-specific Features (LIFT) achieves state-of-the-art performance because the most distinctive features are constructed for each label. Clustering centers play an essential role in label-specific feature conversion. However, LIFT does not consider the relationship between positive and negative instances, and clustering centers on the training dataset is inconsistent with that of the original instances. In this paper, we propose a new algorithm called SMOTE_DFL, which can get better clustering centers through two strategies: 1) The spectral clustering algorithm SIA is introduced to focus on the local structure between positive and negative instances; 2) By oversampling training sample, the clustering center of training sample is close to that of the whole. Extensive experiments are conducted on three datasets. The results validate the superiority of SMOTE_DFL to other algorithms.

Cognitive theories of object recognition have traditionally emphasized structural components (Biederman, 1987; Grossberg & Mingolla, 1985). The idea that object recognition is largely driven by shape was advantageous to theory building because of its economy (i.e., only a single dimension needed to be attended and there are a finite number of mutually exclusive components). However, recent work provides evidence that surface level information (e.g., object color) is readily used in object recognition (Rossion & Pourtois, 2004; Tanaka & Presnell, 1999; Therriault et al., 2009; & Naor-Raz et al., 2003). The purpose of this chapter is two-fold: to present results from experiments that more closely examine color’s influence on object recognition and to reconcile these results with traditional theories of object recognition. Section 2 contains a historical overview of the claims made between strucutral (i.e., edge) and view-point dependent (i.e., surface + edge) characterizations of object recognition. Although the debate may be subsiding over the status of viewpoint invariance, many open questions remain concerning how color contributes to the processing and recognition of objects. Section 3 reviews conflicting research on the role of color in object recognition. Some studies fail to find any effects of color upon recognition, others find evidence for only high color diagnostic objects, and still others find that color readily influences recognition. This section concludes by offering some explanations for differences in obtained results. Section 4 presents a recent set of experiments from my lab exploring the role of color in recognition, conceptualization, and language use. Most striking, the results from four different experiments are identical with respect to color. The presentation of correctly colored items always enhanced recognition and conceptualization of the objects. In Section 5, the early conceptual analogy used in object recognition (i.e., speech segmentation) is reviewed and updated. I propose that object recognition is more anlagous to word recognition in reading. This is a more apt analogy because it can accomodate both structural and view-point evidence. Finally, Section 6 argues that evidence calls for a more nuanced, flexible and integrated theory of object recognition, one that includes both bottom-up and top-down processing. The chapter concludes that the study of color vision is a fruitful area from which to gain a deeper understanding of object recognition generally; and that this pursuit would benefit greatly from the contribution of disciplines beyond cognition (e.g., neuroscience, biology, and linguistics).